class_docstr: A metric to measure the fitness of a program.
  
  This object is able to be called with NumPy vectorized arguments and return
  a resulting floating point score quantifying the quality of the program's
  representation of the true relationship.
  
  Parameters
  ----------
  function : callable
      A function with signature function(y, y_pred, sample_weight) that
      returns a floating point number. Where `y` is the input target y
      vector, `y_pred` is the predicted values from the genetic program, and
      sample_weight is the sample_weight vector.
  
  greater_is_better : bool
      Whether a higher value from `function` indicates a better fit. In
      general this would be False for metrics indicating the magnitude of
      the error, and True for metrics indicating the quality of fit.
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code: def __call__(self, *args):
      return self.function(*args)
  
file_path: gplearn/gplearn/fitness.py
imports: import numbers
  import numpy
  from joblib import wrap_non_picklable_objects
  from scipy.stats import rankdata
label_desc: Use image node_id 2 for calling the _Fitness obj's underlying member method code with example usage: obj.__call__() and returns: self