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Upload pums.py
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pums.py
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@@ -16,6 +16,50 @@ _ENCODING_DICS = {
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"50000+.": 1
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}
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}
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DESCRIPTION = "Pums dataset."
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990"
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@@ -108,7 +152,7 @@ class Pums(datasets.GeneratorBasedBuilder):
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]
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def _generate_examples(self, filepath: str):
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data = pandas.read_csv(filepath)
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data = self.preprocess(data)
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for row_id, row in data.iterrows():
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@@ -117,6 +161,8 @@ class Pums(datasets.GeneratorBasedBuilder):
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yield row_id, data_row
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def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
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for feature in _ENCODING_DICS:
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encoding_function = partial(self.encode, feature)
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data.loc[:, feature] = data[feature].apply(encoding_function)
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"50000+.": 1
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}
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}
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_BASE_FEATURE_NAMES = [
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"age",
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"class_of_worker",
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"detailed_industry_recode",
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"detailed_occupation_recode",
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"education",
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"wage_per_hour",
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"enroll_in_edu_inst_last_wk",
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"marital_stat",
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"major_industry_code",
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"major_occupation_code",
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"race",
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"hispanic_origin",
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"sex",
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"member_of_a_labor_union",
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"reason_for_unemployment",
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"full_or_part_time_employment_stat",
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"capital_gains",
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"capital_losses",
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"dividends_from_stocks",
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"tax_filer_stat",
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"region_of_previous_residence",
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"state_of_previous_residence",
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"detailed_household_and_family_stat",
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"detailed_household_summary_in_household",
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"instance_weight",
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"migration_code_change_in_msa",
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"migration_code_change_in_reg",
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"migration_code_move_within_reg",
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"live_in_this_house_1_year_ago",
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"migration_prev_res_in_sunbelt",
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"num_persons_worked_for_employer",
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"family_members_under_18",
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"country_of_birth_father",
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"country_of_birth_mother",
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"country_of_birth_self",
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"citizenship",
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"own_business_or_self_employed",
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"fill_inc_questionnaire_for_veteran_admin",
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"veterans_benefits",
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"weeks_worked_in_year",
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"year",
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"class",
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]
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DESCRIPTION = "Pums dataset."
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990"
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]
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def _generate_examples(self, filepath: str):
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data = pandas.read_csv(filepath, header=None)
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data = self.preprocess(data)
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for row_id, row in data.iterrows():
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yield row_id, data_row
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def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
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data.columns = _BASE_FEATURE_NAMES
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for feature in _ENCODING_DICS:
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encoding_function = partial(self.encode, feature)
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data.loc[:, feature] = data[feature].apply(encoding_function)
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