class_docstr: MEPS Dataset.
  
  See :file:`aif360/data/raw/meps/README.md`.
code: def __init__(
      self,
      label_name="UTILIZATION",
      favorable_classes=[1.0],
      protected_attribute_names=["RACE"],
      privileged_classes=[["White"]],
      instance_weights_name="PERWT15F",
      categorical_features=[
          "REGION",
          "SEX",
          "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",
      ],
      features_to_keep=[
          "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",
          "UTILIZATION",
          "PERWT15F",
      ],
      features_to_drop=[],
      na_values=[],
      custom_preprocessing=default_preprocessing,
      metadata=default_mappings,
  ):
      filepath = os.path.join(
          os.path.dirname(os.path.abspath(__file__)),
          "..",
          "data",
          "raw",
          "meps",
          "h181.csv",
      )
  
      try:
          df = pd.read_csv(filepath, sep=",", na_values=na_values)
      except IOError as err:
          print("IOError: {}".format(err))
          print("To use this class, please follow the instructions in:")
          print(
              "\n\t{}\n".format(
                  os.path.abspath(
                      os.path.join(
                          os.path.abspath(__file__),
                          "..",
                          "..",
                          "data",
                          "raw",
                          "meps",
                          "README.md",
                      )
                  )
              )
          )
          print(
              "\n to download and convert the 2015 data and place the final h181.csv file, as-is, in the folder:"
          )
          print(
              "\n\t{}\n".format(
                  os.path.abspath(
                      os.path.join(
                          os.path.abspath(__file__),
                          "..",
                          "..",
                          "data",
                          "raw",
                          "meps",
                      )
                  )
              )
          )
          import sys
  
          sys.exit(1)
  
      super(MEPSDataset19, self).__init__(
          df=df,
          label_name=label_name,
          favorable_classes=favorable_classes,
          protected_attribute_names=protected_attribute_names,
          privileged_classes=privileged_classes,
          instance_weights_name=instance_weights_name,
          categorical_features=categorical_features,
          features_to_keep=features_to_keep,
          features_to_drop=features_to_drop,
          na_values=na_values,
          custom_preprocessing=custom_preprocessing,
          metadata=metadata,
      )
  
imports: import os
  import pandas
  from aif360.datasets import StandardDataset
label_desc: Use image node_id 1 to create a new MEPSDataset19 object from inherited base classes: StandardDataset with example: obj = MEPSDataset19(label_name, favorable_classes, protected_attribute_names, privileged_classes, instance_weights_name, categorical_features, features_to_keep, features_to_drop, na_values, custom_preprocessing, metadata)