(preprocess=False)
Fetch a subset of the `MEPS`_ dataset from aif360 and add fairness info.

    It contains information collected on a nationally representative sample
    of the civilian noninstitutionalized population of the United States,
    specifically reported medical expenditures and civilian demographics.
    This dataframe corresponds to data from panel 20 from the year 2015.
    Without preprocessing, the dataframe contains 18849 rows and 1825 columns.
    (With preprocessing the dataframe contains 17570 rows and 138 columns.)
    There is one protected attribute, race, and the disparate impact is 0.493
    if preprocessing is not applied and 0.488 if preprocessing is applied.
    The data includes numeric and categorical columns, with some missing values.

    Note: in order to use this dataset, be sure to follow the instructions
    found in the `AIF360 documentation`_ and accept the corresponding license agreement.

    .. _`MEPS`:  https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181
    .. _`AIF360 documentation`: https://github.com/Trusted-AI/AIF360/tree/master/aif360/data/raw/meps

    Parameters
    ----------
    preprocess : boolean, optional, default False

      If True,
      encode protected attribute in X corresponding to race as 0 or 1
      to indicate privileged groups;
      encode labels in y as 0 or 1 to indicate faborable outcomes;
      rename columns that are panel or round-specific;
      drop columns such as ID columns that are not relevant to the task at hand;
      and drop rows where features are unknown.

    Returns
    -------
    result : tuple

      - item 0: pandas Dataframe

          Features X, including both protected and non-protected attributes.

      - item 1: pandas Series

          Labels y.

      - item 3: fairness_info

          JSON meta-data following the format understood by fairness metrics
          and mitigation operators in `lale.lib.aif360`.
Fetch a subset of the `MEPS`_ dataset from aif360 and add fairness info.
Fetch a subset of the MEPS _ dataset from aif360 and add fairness info .
def fetch_meps_panel20_fy2015_df(preprocess=False):
    """
    Fetch a subset of the `MEPS`_ dataset from aif360 and add fairness info.

    It contains information collected on a nationally representative sample
    of the civilian noninstitutionalized population of the United States,
    specifically reported medical expenditures and civilian demographics.
    This dataframe corresponds to data from panel 20 from the year 2015.
    Without preprocessing, the dataframe contains 18849 rows and 1825 columns.
    (With preprocessing the dataframe contains 17570 rows and 138 columns.)
    There is one protected attribute, race, and the disparate impact is 0.493
    if preprocessing is not applied and 0.488 if preprocessing is applied.
    The data includes numeric and categorical columns, with some missing values.

    Note: in order to use this dataset, be sure to follow the instructions
    found in the `AIF360 documentation`_ and accept the corresponding license agreement.

    .. _`MEPS`:  https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181
    .. _`AIF360 documentation`: https://github.com/Trusted-AI/AIF360/tree/master/aif360/data/raw/meps

    Parameters
    ----------
    preprocess : boolean, optional, default False

      If True,
      encode protected attribute in X corresponding to race as 0 or 1
      to indicate privileged groups;
      encode labels in y as 0 or 1 to indicate faborable outcomes;
      rename columns that are panel or round-specific;
      drop columns such as ID columns that are not relevant to the task at hand;
      and drop rows where features are unknown.

    Returns
    -------
    result : tuple

      - item 0: pandas Dataframe

          Features X, including both protected and non-protected attributes.

      - item 1: pandas Series

          Labels y.

      - item 3: fairness_info

          JSON meta-data following the format understood by fairness metrics
          and mitigation operators in `lale.lib.aif360`.
    """
    if preprocess:
        dataset = aif360.datasets.MEPSDataset20()
        return _get_pandas_and_fairness_info_from_meps_dataset(dataset)
    else:
        return _fetch_meps_raw_df(Panel.PANEL20, FiscalYear.FY2015)
def fetch_meps_panel20_fy2015_df ( preprocess = False ) : if preprocess : dataset = aif360 . datasets . MEPSDataset20 ( ) return _get_pandas_and_fairness_info_from_meps_dataset ( dataset ) else : return _fetch_meps_raw_df ( Panel . PANEL20 , FiscalYear . FY2015 )