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: Decodes OHE mapping into the original categories. Since this approach uses an argmax, decoding flexibly works either on logits or an explicitly OHE vector.
  
  :param: encoded_data:
  :returns Returns the original category names for encoded data.
docstr_tok: ["Decodes","OHE","mapping","into","the","original","categories",".","Since","this","approach","uses","an","argmax",",","decoding","flexibly","works","either","on","logits","or","an","explicitly","OHE","vector",".",":","param",":","encoded_data",":",":","returns","Returns","the","original","category","names","for","encoded","data","."]
code: def decode(self, encoded_data: torch.Tensor):
      """
      Decodes OHE mapping into the original categories. Since this approach uses an argmax, decoding flexibly works either on logits or an explicitly OHE vector.
  
      :param: encoded_data:
      :returns Returns the original category names for encoded data.
      """  # noqa
      encoded_data_list = encoded_data.tolist()
      ret = []
  
      for vector in encoded_data_list:
          all_zeros = not np.any(vector)
          if not self.use_unknown and all_zeros:
              ret.append(_UNCOMMON_WORD)
          else:
              ret.append(self.rev_map[np.argmax(vector)])
  
      return ret
  
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 4 for calling the OneHotEncoder obj's underlying member method code with example usage: obj.decode(encoded_data) and returns: ret