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: Given a vector, normalizes so that the sum of elements is 1, using softmax.
  
  :param vec: Assigned weights for each category
docstr_tok: ["Given","a","vector",",","normalizes","so","that","the","sum","of","elements","is","1",",","using","softmax",".",":","param","vec",":","Assigned","weights","for","each","category"]
code: def _norm_vec(vec: List[float]):
      """
      Given a vector, normalizes so that the sum of elements is 1, using softmax.
  
      :param vec: Assigned weights for each category
      """
      return softmax(vec)
  
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 6 for calling the OneHotEncoder obj's underlying member method code with example usage: obj._norm_vec(vec) and returns: softmax