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Long-Term-Care-Aggregated-Data.py
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@@ -54,71 +54,72 @@ _URLS = {
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class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
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"""Dataset for insurance 'incidence' and 'termination' data."""
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# This function should return the dataset info
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def _info(self):
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def _split_generators(self, dl_manager):
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# URLs of the raw CSV files on GitHub using the raw content feature
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@@ -134,30 +135,37 @@ class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
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return [
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datasets.SplitGenerator(
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name=
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=
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gen_kwargs={
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),
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]
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dataframe = pd.read_csv(filepath)
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# Determine the type of data we're generating examples for
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feature_columns = []
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if 'incidence' in split:
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feature_columns = [
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"Group_Indicator", "Gender", "Issue_Age_Bucket", "Incurred_Age_Bucket",
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"Issue_Year_Bucket", "Policy_Year", "Marital_Status", "Premium_Class",
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@@ -176,6 +184,5 @@ class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
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]
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for idx, row in dataframe.iterrows():
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# Generate the feature dictionary dynamically based on the columns
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feature_dict = {column: row[column] for column in feature_columns}
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yield idx, feature_dict
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class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
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"""Dataset for insurance 'incidence' and 'termination' data."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="incidence", version=datasets.Version("1.0.0"), description="This part of the dataset includes incidence features"),
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datasets.BuilderConfig(name="termination", version=datasets.Version("1.0.0"), description="This part of the dataset includes termination features"),
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]
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def _info(self):
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if self.config.name == "incidence":
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features = datasets.Features({
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"Group_Indicator": datasets.Value("string"),
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"Gender": datasets.Value("string"),
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"Issue_Age_Bucket": datasets.Value("string"),
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"Incurred_Age_Bucket": datasets.Value("string"),
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"Issue_Year_Bucket": datasets.Value("string"),
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"Policy_Year": datasets.Value("string"),
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"Marital_Status": datasets.Value("string"),
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"Premium_Class": datasets.Value("string"),
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"Underwriting_Type": datasets.Value("string"),
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"Coverage_Type_Bucket": datasets.Value("string"),
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"Tax_Qualification_Status": datasets.Value("string"),
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"Inflation_Rider": datasets.Value("string"),
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"Rate_Increase_Flag": datasets.Value("string"),
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"Restoration_of_Benefits": datasets.Value("string"),
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"NH_Orig_Daily_Ben_Bucket": datasets.Value("string"),
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"ALF_Orig_Daily_Ben_Bucket": datasets.Value("string"),
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"HHC_Orig_Daily_Ben_Bucket": datasets.Value("string"),
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"NH_Ben_Period_Bucket": datasets.Value("string"),
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"ALF_Ben_Period_Bucket": datasets.Value("string"),
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"HHC_Ben_Period_Bucket": datasets.Value("string"),
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"NH_EP_Bucket": datasets.Value("string"),
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"ALF_EP_Bucket": datasets.Value("string"),
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"HHC_EP_Bucket": datasets.Value("string"),
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"Region": datasets.Value("string"),
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"Active_Exposure": datasets.Value("float64"),
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"Total_Exposure": datasets.Value("float64"),
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"Claim_Count": datasets.Value("int32"),
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"Count_NH": datasets.Value("int32"),
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"Count_ALF": datasets.Value("int32"),
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"Count_HHC": datasets.Value("int32"),
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"Count_Unk": datasets.Value("int32"),
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})
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elif self.config.name == "termination":
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features = datasets.Features({
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"Gender": datasets.Value("string"),
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"Incurred_Age_Bucket": datasets.Value("string"),
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"Incurred_Year_Bucket": datasets.Value("string"),
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"Claim_Type": datasets.Value("string"),
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"Region": datasets.Value("string"),
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"Diagnosis_Category": datasets.Value("string"),
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"Claim_Duration": datasets.Value("int64"),
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"Exposure": datasets.Value("int64"),
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"Deaths": datasets.Value("int64"),
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"Recovery": datasets.Value("int64"),
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"Terminations": datasets.Value("int64"),
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"Benefit_Expiry": datasets.Value("int64"),
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"Others_Terminations": datasets.Value("int64"),
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})
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else:
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raise ValueError(f"BuilderConfig name not recognized: {self.config.name}")
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage="https://www.soa.org/resources/experience-studies/2020/2000-2016-ltc-aggregate-database/",
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citation="Please cite this dataset as: Society of Actuaries (SOA). (2020). Long Term Care Insurance Aggregate Experience Data, 2000-2016."
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)
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def _split_generators(self, dl_manager):
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# URLs of the raw CSV files on GitHub using the raw content feature
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return [
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datasets.SplitGenerator(
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name="train_incidence",
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gen_kwargs={
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"filepath": downloaded_files["train_incidence"],
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"split": "incidence"
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},
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),
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datasets.SplitGenerator(
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name="validation_incidence",
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gen_kwargs={
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"filepath": downloaded_files["validation_incidence"],
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"split": "incidence"
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},
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),
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datasets.SplitGenerator(
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name="train_termination",
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gen_kwargs={
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"filepath": downloaded_files["train_termination"],
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"split": "termination"
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},
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),
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datasets.SplitGenerator(
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name="validation_termination",
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gen_kwargs={
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"filepath": downloaded_files["validation_termination"],
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"split": "termination"
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},
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),
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]
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def _generate_examples(self, incidence_filepath, termination_filepath, split):
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if self.config.name == "incidence":
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dataframe = pd.read_csv(incidence_filepath)
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feature_columns = [
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"Group_Indicator", "Gender", "Issue_Age_Bucket", "Incurred_Age_Bucket",
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"Issue_Year_Bucket", "Policy_Year", "Marital_Status", "Premium_Class",
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]
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for idx, row in dataframe.iterrows():
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feature_dict = {column: row[column] for column in feature_columns}
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yield idx, feature_dict
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