class_docstr: Creates a one-hot encoding (OHE) for categorical data. One-hot encoding represents categorical information as a vector where each individual dimension corresponds to a category. A category has a 1:1 mapping between dimension indicated by a "1" in that position. For example, imagine 3 categories, :math:`A`, :math:`B`, and :math:`C`; these can be represented as follows:
  
  .. math::
  
     A &= [1, 0, 0] \
     B &= [0, 1, 0] \
     C &= [0, 0, 1]
  
  The OHE encoder operates in 2 modes:
      (1) "use_unknown=True": Makes an :math:`N+1` length vector for :math:`N` categories, the first index always corresponds to the unknown category.
  
      (2) "use_unknown=False": Makes an :math:`N` length vector for :math:`N` categories, where an empty vector of 0s indicates an unknown/missing category.
  
  An encoder can represent a feature column or target column; in this case it represents a target, `is_target` is `True`, and `target_weights`. The `target_weights` parameter enables users to specify how heavily each class should be weighted within a mixer - useful in imbalanced classes.
  
  By default, `dataprep_ml.StatisticalAnalysis` phase will provide `target_weights` as the relative fraction of each class in the data which is important for imbalanced populations; for example, suppose there is a 80/05/15 imbalanced representation across 3 different classes - `target_weights` will be a vector as such::
  
  target_weights = {"class1": 0.8, "class2": 0.05, "class3": 0.15}
  
  Users should note that models will be presented with the inverse of the target weights, `inv_target_weights`, which will perform the 1/target_value_per_class operation. **This means large values will result in small weights for the model**.
docstr: :param is_target: True if this encoder featurizes the target column
  :param target_weights: Percentage of total population represented by each category (between [0, 1]).
  :param mode: True uses an extra dimension to account for unknown/out-of-distribution categories
docstr_tok: [":","param","is_target",":","True","if","this","encoder","featurizes","the","target","column",":","param","target_weights",":","Percentage","of","total","population","represented","by","each","category","(","between","[","0",",","1","]",")",".",":","param","mode",":","True","uses","an","extra","dimension","to","account","for","unknown\/out-of-distribution","categories"]
code: def __init__(
      self,
      is_target: bool = False,
      target_weights: Dict[str, float] = None,
      use_unknown: bool = True,
  ):
      """
      :param is_target: True if this encoder featurizes the target column
      :param target_weights: Percentage of total population represented by each category (between [0, 1]).
      :param mode: True uses an extra dimension to account for unknown/out-of-distribution categories
      """  # noqa
  
      super().__init__(is_target)
      self.map = None  # category name -> index
      self.rev_map = None  # index -> category name
      self.use_unknown = use_unknown
  
      # Weight-balance info if encoder represents target
      self.target_weights = None
      self.index_weights = None  # vector-weights, mapped by class id
      if self.is_target:
          self.target_weights = deepcopy(target_weights)
  
file_path: lightwood/lightwood/encoder/categorical/onehot.py
imports: from copy import deepcopy
  from typing import Dict, List, Iterable, Tuple
  import torch
  import numpy
  from scipy.special import softmax
  from lightwood.helpers.log import log
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.constants import _UNCOMMON_WORD
label_desc: Use image node_id 1 to create a new OneHotEncoder object from inherited base classes: BaseEncoder with example: obj = OneHotEncoder(is_target, target_weights, use_unknown)