class_docstr: Apply a log transformation to an endogenous array
  
  When ``y`` is your endogenous array, the log transform is
  ``log(y + lmbda)``
  
  Parameters
  ----------
  
  lmbda : float, optional (default=0.)
      The value to add to ``y`` to make it non-negative. If, after adding
      ``lmbda``, there are still negative values, a ValueError will be
      raised.
  
  neg_action : str, optional (default="raise")
      How to respond if any values in ``y <= 0`` after adding ``lmbda``.
      One of ('raise', 'warn', 'ignore'). If anything other than 'raise',
      values <= 0 will be truncated to the value of ``floor``.
  
  floor : float, optional (default=1e-16)
      A positive value that truncate values to if there are values in ``y``
      that are zero or negative and ``neg_action`` is not 'raise'. Note that
      if values are truncated, invertibility will not be preserved, and the
      transformed array may not be perfectly inverse-transformed.
code: def __init__(self, lmbda=0, neg_action="raise", floor=1e-16):
      super().__init__(neg_action=neg_action, floor=floor)
  
      # See: https://github.com/alkaline-ml/pmdarima/issues/407
      self.lmbda = 0
      self.lmbda2 = lmbda
  
imports: from .boxcox import BoxCoxEndogTransformer
label_desc: Use image node_id 1 to create a new LogEndogTransformer object from inherited base classes: BoxCoxEndogTransformer with example: obj = LogEndogTransformer(lmbda, neg_action, floor)