class_docstr: The numeric encoder takes numbers (float or integer) and converts it into tensors of the form:
  ``[0 if the number is none, otherwise 1, 1 if the number is positive, otherwise 0, natural_log(abs(number)), number/absolute_mean]``
  
  This representation is: ``[1 if the number is positive, otherwise 0, natural_log(abs(number)), number/absolute_mean]]`` if encoding target values, since target values can't be none.
  
  The ``absolute_mean`` is computed in the ``prepare`` method and is just the mean of the absolute values of all numbers feed to prepare (which are not none)
  
  ``none`` stands for any number that is an actual python ``None`` value or any sort of non-numeric value (a string, nan, inf)
class_docstr_tok: ["The","numeric","encoder","takes","numbers","(","float","or","integer",")","and","converts","it","into","tensors","of","the","form",":","``","[","0","if","the","number","is","none",",","otherwise","1",",","1","if","the","number","is","positive",",","otherwise","0",",","natural_log","(","abs","(","number",")",")",",","number\/absolute_mean","]","``","This","representation","is",":","``","[","1","if","the","number","is","positive",",","otherwise","0",",","natural_log","(","abs","(","number",")",")",",","number\/absolute_mean","]","]","``","if","encoding","target","values",",","since","target","values","ca","n't","be","none",".","The","``","absolute_mean","``","is","computed","in","the","``","prepare","``","method","and","is","just","the","mean","of","the","absolute","values","of","all","numbers","feed","to","prepare","(","which","are","not","none",")","``","none","``","stands","for","any","number","that","is","an","actual","python","``","None","``","value","or","any","sort","of","non-numeric","value","(","a","string",",","nan",",","inf",")"]
docstr: :param data_type: The data type of the number (integer, float, quantity)
  :param is_target: Indicates whether the encoder refers to a target column or feature column (True==target)
  :param positive_domain: Forces the encoder to always output positive values
docstr_tok: [":","param","data_type",":","The","data","type","of","the","number","(","integer",",","float",",","quantity",")",":","param","is_target",":","Indicates","whether","the","encoder","refers","to","a","target","column","or","feature","column","(","True==target",")",":","param","positive_domain",":","Forces","the","encoder","to","always","output","positive","values"]
code: def __init__(
      self,
      data_type: dtype = None,
      is_target: bool = False,
      positive_domain: bool = False,
  ):
      """
      :param data_type: The data type of the number (integer, float, quantity)
      :param is_target: Indicates whether the encoder refers to a target column or feature column (True==target)
      :param positive_domain: Forces the encoder to always output positive values
      """
      super().__init__(is_target)
      self._abs_mean = None
      self.positive_domain = positive_domain
      self.decode_log = False
      self.output_size = 4 if not self.is_target else 3
  
file_path: lightwood/lightwood/encoder/numeric/numeric.py
imports: import math
  from typing import Union
  import torch
  import numpy
  import pandas
  from type_infer.dtype import dtype
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.general import is_none
label_desc: Use image node_id 1 to create a new NumericEncoder object from inherited base classes: BaseEncoder with example: obj = NumericEncoder(data_type, is_target, positive_domain)