code: def iter_arff(
      filepath_or_buffer,
      target: str | list[str] | None = None,
      compression="infer",
      sparse=False,
  ) -> base.typing.Stream:
      """Iterates over rows from an ARFF file.
  
      Parameters
      ----------
      filepath_or_buffer
          Either a string indicating the location of a file, or a buffer object that has a
          `read` method.
      target
          Name(s) of the target field. If `None`, then the target field is ignored. If a list of
          names is passed, then a dictionary is returned instead of a single value.
      compression
          For on-the-fly decompression of on-disk data. If this is set to 'infer' and
          `filepath_or_buffer` is a path, then the decompression method is inferred for the
          following extensions: '.gz', '.zip'.
      sparse
          Whether the data is sparse or not.
  
      Examples
      --------
  
      >>> cars = '''
      ... @relation CarData
      ... @attribute make {Toyota, Honda, Ford, Chevrolet}
      ... @attribute model string
      ... @attribute year numeric
      ... @attribute price numeric
      ... @attribute mpg numeric
      ... @data
      ... Toyota, Corolla, 2018, 15000, 30.5
      ... Honda, Civic, 2019, 16000, 32.2
      ... Ford, Mustang, 2020, 25000, 25.0
      ... Chevrolet, Malibu, 2017, 18000, 28.9
      ... Toyota, Camry, 2019, 22000, 29.8
      ... '''
      >>> with open('cars.arff', mode='w') as f:
      ...     _ = f.write(cars)
  
      >>> from river import stream
  
      >>> for x, y in stream.iter_arff('cars.arff', target='price'):
      ...     print(x, y)
      {'make': 'Toyota', 'model': ' Corolla', 'year': 2018.0, 'mpg': 30.5} 15000.0
      {'make': 'Honda', 'model': ' Civic', 'year': 2019.0, 'mpg': 32.2} 16000.0
      {'make': 'Ford', 'model': ' Mustang', 'year': 2020.0, 'mpg': 25.0} 25000.0
      {'make': 'Chevrolet', 'model': ' Malibu', 'year': 2017.0, 'mpg': 28.9} 18000.0
      {'make': 'Toyota', 'model': ' Camry', 'year': 2019.0, 'mpg': 29.8} 22000.0
  
      Finally, let's delete the example file.
  
      >>> import os; os.remove('cars.arff')
  
      ARFF files support sparse data. Let's create a sparse ARFF file.
  
      >>> sparse = '''
      ... % traindata
      ... @RELATION "traindata: -C 6"
      ... @ATTRIBUTE y0 {0, 1}
      ... @ATTRIBUTE y1 {0, 1}
      ... @ATTRIBUTE y2 {0, 1}
      ... @ATTRIBUTE y3 {0, 1}
      ... @ATTRIBUTE y4 {0, 1}
      ... @ATTRIBUTE y5 {0, 1}
      ... @ATTRIBUTE X0 NUMERIC
      ... @ATTRIBUTE X1 NUMERIC
      ... @ATTRIBUTE X2 NUMERIC
      ... @DATA
      ... { 3 1,6 0.863382,8 0.820094 }
      ... { 2 1,6 0.659761 }
      ... { 0 1,3 1,6 0.437881,8 0.818882 }
      ... { 2 1,6 0.676477,7 0.724635,8 0.755123 }
      ... '''
  
      >>> with open('sparse.arff', mode='w') as f:
      ...     _ = f.write(sparse)
  
      In addition, we'll specify that there are several target fields.
  
      >>> arff_stream = stream.iter_arff(
      ...     'sparse.arff',
      ...     target=['y0', 'y1', 'y2', 'y3', 'y4', 'y5'],
      ...     sparse=True
      ... )
  
      >>> for x, y in arff_stream:
      ...     print(x)
      ...     print(y)
      {'X0': '0.863382', 'X2': '0.820094'}
      {'y0': 0, 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}
      {'X0': '0.659761'}
      {'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}
      {'X0': '0.437881', 'X2': '0.818882'}
      {'y0': '1', 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0}
      {'X0': '0.676477', 'X1': '0.724635', 'X2': '0.755123'}
      {'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0}
  
      This function can also deal with missing features in non-sparse data. These are indicated with
      a question mark.
  
      >>> data = '''
      ... @relation giveMeLoan-weka.filters.unsupervised.attribute.Remove-R1
      ... @attribute RevolvingUtilizationOfUnsecuredLines numeric
      ... @attribute age numeric
      ... @attribute NumberOfTime30-59DaysPastDueNotWorse numeric
      ... @attribute DebtRatio numeric
      ... @attribute MonthlyIncome numeric
      ... @attribute NumberOfOpenCreditLinesAndLoans numeric
      ... @attribute NumberOfTimes90DaysLate numeric
      ... @attribute NumberRealEstateLoansOrLines numeric
      ... @attribute NumberOfTime60-89DaysPastDueNotWorse numeric
      ... @attribute NumberOfDependents numeric
      ... @attribute isFraud {0,1}
      ... @data
      ... 0.213179,74,0,0.375607,3500,3,0,1,0,1,0
      ... 0.305682,57,0,5710,?,8,0,3,0,0,0
      ... 0.754464,39,0,0.20994,3500,8,0,0,0,0,0
      ... 0.116951,27,0,46,?,2,0,0,0,0,0
      ... 0.189169,57,0,0.606291,23684,9,0,4,0,2,0
      ... '''
  
      >>> with open('data.arff', mode='w') as f:
      ...     _ = f.write(data)
  
      >>> for x, y in stream.iter_arff('data.arff', target='isFraud'):
      ...     print(len(x))
      10
      9
      10
      9
      10
  
      References
      ----------
      [^1]: [ARFF format description from Weka](https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/)
  
      """
  
      # If a file is not opened, then we open it
      buffer = filepath_or_buffer
      if not hasattr(buffer, "read"):
          buffer = utils.open_filepath(buffer, compression)
  
      try:
          rel, attrs = read_header(buffer)
      except ValueError as e:
          msg = f"Error while parsing header, error was: {e}"
          raise scipy.io.arff.ParseArffError(msg)
  
      names = [attr.name for attr in attrs]
      # HACK: it's a bit hacky to rely on class name to determine what casting to apply
      casts = [
          float
          if attr.__class__.__name__ == "NumericAttribute"
          else None
          for attr in attrs
      ]
  
      for r in buffer:
          if len(r) == 0:
              continue
  
          # Read row
          if sparse:
              x = {}
              for s in r.rstrip()[1:-1].strip().split(","):
                  name_index, val = s.split(" ", 1)
                  x[names[int(name_index)]] = val
          else:
              x = {
                  name: cast(val) if cast else val
                  for name, cast, val in zip(
                      names, casts, r.rstrip().split(",")
                  )
                  if val != "?"
              }
  
          # Handle target
          y = None
          if target is not None:
              if isinstance(target, list):
                  y = {name: x.pop(name, 0) for name in target}
              else:
                  y = x.pop(target) if target else None
  
          yield x, y
  
      # Close the file if we opened it
      if buffer is not filepath_or_buffer:
          buffer.close()
  
imports: from __future__ import annotations
  import scipy.io.arff
  from scipy.io.arff._arffread import read_header
  from river import base
  from .None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: iter_arff(filepath_or_buffer, target, compression, sparse) without return types