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* allow explicit arrays for prot_attr, target * add MEPS and violent recidivism datasets * option to skip cache * binary_race only affects protected attribute unless numeric_only * remove unused categories after dropping * minimum python version >= 3.7; scikit-learn >= 1.0
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| from io import BytesIO | ||
| import os | ||
| from zipfile import ZipFile | ||
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| import pandas as pd | ||
| import requests | ||
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| from aif360.sklearn.datasets.utils import standardize_dataset | ||
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| # cache location | ||
| DATA_HOME_DEFAULT = os.path.join(os.path.dirname(os.path.abspath(__file__)), | ||
| '..', 'data', 'raw') | ||
| MEPS_URL = "https://meps.ahrq.gov/mepsweb/data_files/pufs" | ||
| PROMPT = """ | ||
| By using this function you acknowledge the responsibility for reading and | ||
| abiding by any copyright/usage rules and restrictions as stated on the MEPS web | ||
| site (https://meps.ahrq.gov/data_stats/data_use.jsp). | ||
| Continue [y/n]? > """ | ||
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| def fetch_meps(panel, *, accept_terms=None, data_home=None, cache=True, | ||
| usecols=['REGION', 'AGE', 'SEX', 'RACE', 'MARRY', 'FTSTU', | ||
| 'ACTDTY', 'HONRDC', 'RTHLTH', 'MNHLTH', 'HIBPDX', | ||
| 'CHDDX', 'ANGIDX', 'MIDX', 'OHRTDX', 'STRKDX', 'EMPHDX', | ||
| 'CHBRON', 'CHOLDX', 'CANCERDX', 'DIABDX', 'JTPAIN', | ||
| 'ARTHDX', 'ARTHTYPE', 'ASTHDX', 'ADHDADDX', 'PREGNT', | ||
| 'WLKLIM', 'ACTLIM', 'SOCLIM', 'COGLIM', 'DFHEAR42', | ||
| 'DFSEE42', 'ADSMOK42', 'PCS42', 'MCS42', 'K6SUM42', | ||
| 'PHQ242', 'EMPST', 'POVCAT', 'INSCOV'], | ||
| dropcols=None, numeric_only=False, dropna=True): | ||
| """Load the Medical Expenditure Panel Survey (MEPS) dataset. | ||
| Note: | ||
| For descriptions of the dataset features, see the `data codebook | ||
| <https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H181>`_. | ||
| Args: | ||
| panel ({19, 20, 21}): Panel number (only 19, 20, and 21 are currently | ||
| supported). | ||
| accept_terms (bool, optional): Bypass terms prompt. Note: by setting | ||
| this to ``True``, you acknowledge responsibility for reading and | ||
| accepting the MEPS usage terms. | ||
| data_home (string, optional): Specify another download and cache folder | ||
| for the datasets. By default all AIF360 datasets are stored in | ||
| 'aif360/sklearn/data/raw' subfolders. | ||
| cache (bool): Whether to cache downloaded datasets. | ||
| usecols (single label or list-like, optional): Feature column(s) to | ||
| keep. All others are dropped. | ||
| dropcols (single label or list-like, optional): Feature column(s) to | ||
| drop. | ||
| numeric_only (bool): Drop all non-numeric feature columns. | ||
| dropna (bool): Drop rows with NAs. | ||
| Returns: | ||
| namedtuple: Tuple containing X and y for the MEPS dataset accessible by | ||
| index or name. | ||
| """ | ||
| if panel not in {19, 20, 21}: | ||
| raise ValueError("only panels 19, 20, and 21 are currently supported.") | ||
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| fname = 'h192' if panel == 21 else 'h181' | ||
| cache_path = os.path.join(data_home or DATA_HOME_DEFAULT, fname + '.csv') | ||
| if cache and os.path.isfile(cache_path): | ||
| df = pd.read_csv(cache_path) | ||
| else: | ||
| # skip prompt if user chooses | ||
| accept = accept_terms or input(PROMPT) | ||
| if accept != 'y' and accept != True: | ||
| raise PermissionError("Terms not agreed.") | ||
| rawz = requests.get(os.path.join(MEPS_URL, fname + 'ssp.zip')).content | ||
| with ZipFile(BytesIO(rawz)) as zf: | ||
| with zf.open(fname + '.ssp') as ssp: | ||
| df = pd.read_sas(ssp, format='xport') | ||
| # TODO: does this cause any differences? | ||
| # reduce storage size | ||
| df = df.apply(pd.to_numeric, errors='ignore', downcast='integer') | ||
| if cache: | ||
| os.makedirs(os.path.dirname(cache_path), exist_ok=True) | ||
| df.to_csv(cache_path, index=None) | ||
| # restrict to correct panel | ||
| df = df[df['PANEL'] == panel] | ||
| # change all 15s to 16s if panel == 21 | ||
| yr = 16 if panel == 21 else 15 | ||
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| # non-Hispanic Whites are marked as WHITE; all others as NON-WHITE | ||
| df['RACEV2X'] = (df['HISPANX'] == 2) & (df['RACEV2X'] == 1) | ||
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| # rename all columns that are panel/round-specific | ||
| df = df.rename(columns={ | ||
| 'FTSTU53X': 'FTSTU', 'ACTDTY53': 'ACTDTY', 'HONRDC53': 'HONRDC', | ||
| 'RTHLTH53': 'RTHLTH', 'MNHLTH53': 'MNHLTH', 'CHBRON53': 'CHBRON', | ||
| 'JTPAIN53': 'JTPAIN', 'PREGNT53': 'PREGNT', 'WLKLIM53': 'WLKLIM', | ||
| 'ACTLIM53': 'ACTLIM', 'SOCLIM53': 'SOCLIM', 'COGLIM53': 'COGLIM', | ||
| 'EMPST53': 'EMPST', 'REGION53': 'REGION', 'MARRY53X': 'MARRY', | ||
| 'AGE53X': 'AGE', f'POVCAT{yr}': 'POVCAT', f'INSCOV{yr}': 'INSCOV', | ||
| f'PERWT{yr}F': 'PERWT', 'RACEV2X': 'RACE'}) | ||
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| df.loc[df.AGE < 0, 'AGE'] = None # set invalid ages to NaN | ||
| cat_cols = ['REGION', 'SEX', 'RACE', 'MARRY', 'FTSTU', 'ACTDTY', 'HONRDC', | ||
| 'RTHLTH', 'MNHLTH', 'HIBPDX', 'CHDDX', 'ANGIDX', 'MIDX', | ||
| 'OHRTDX', 'STRKDX', 'EMPHDX', 'CHBRON', 'CHOLDX', 'CANCERDX', | ||
| 'DIABDX', 'JTPAIN', 'ARTHDX', 'ARTHTYPE', 'ASTHDX', 'ADHDADDX', | ||
| 'PREGNT', 'WLKLIM', 'ACTLIM', 'SOCLIM', 'COGLIM', 'DFHEAR42', | ||
| 'DFSEE42', 'ADSMOK42', 'PHQ242', 'EMPST', 'POVCAT', 'INSCOV', | ||
| # NOTE: education tracking seems to have changed between panels. 'EDUYRDG' | ||
| # was used for panel 19, 'EDUCYR' and 'HIDEG' were used for panels 20 & 21. | ||
| # User may change usecols to include these manually. | ||
| 'EDUCYR', 'HIDEG'] | ||
| if panel == 19: | ||
| cat_cols += ['EDUYRDG'] | ||
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| for col in cat_cols: | ||
| df[col] = df[col].astype('category') | ||
| thresh = 0 if col in ['REGION', 'MARRY', 'ASTHDX'] else -1 | ||
| na_cats = [c for c in df[col].cat.categories if c < thresh] | ||
| df[col] = df[col].cat.remove_categories(na_cats) # set NaN cols to NaN | ||
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| df['SEX'] = df['SEX'].cat.rename_categories({1: 'Male', 2: 'Female'}) | ||
| df['RACE'] = df['RACE'].cat.rename_categories({False: 'Non-White', True: 'White'}) | ||
| df['RACE'] = df['RACE'].cat.reorder_categories(['Non-White', 'White'], ordered=True) | ||
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| # Compute UTILIZATION, binarize it to 0 (< 10) and 1 (>= 10) | ||
| cols = [f'OBTOTV{yr}', f'OPTOTV{yr}', f'ERTOT{yr}', f'IPNGTD{yr}', f'HHTOTD{yr}'] | ||
| util = df[cols].sum(axis=1) | ||
| df['UTILIZATION'] = pd.cut(util, [min(util)-1, 10, max(util)+1], right=False, | ||
| labels=['< 10 Visits', '>= 10 Visits'])#['low', 'high']) | ||
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| return standardize_dataset(df, prot_attr='RACE', target='UTILIZATION', | ||
| sample_weight='PERWT', usecols=usecols, | ||
| dropcols=dropcols, numeric_only=numeric_only, | ||
| dropna=dropna) |
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