class_docstr: Test the OHE vector encoder
  
  NOTE, this is sensitive to the mapping of enc.map. If your unit-tests are failing, check to see if the enc.map order matches what the encoded_data should be; this is based on the mapped-place. Using np.unique() inadvertently sorts the data neatly, hence allowing an intuitive place of which order each category is.
class_docstr_tok: ["Test","the","OHE","vector","encoder","NOTE",",","this","is","sensitive","to","the","mapping","of","enc.map",".","If","your","unit-tests","are","failing",",","check","to","see","if","the","enc.map","order","matches","what","the","encoded_data","should","be",";","this","is","based","on","the","mapped-place",".","Using","np.unique","(",")","inadvertently","sorts","the","data","neatly",",","hence","allowing","an","intuitive","place","of","which","order","each","category","is","."]
docstr: Check probability calculation where `use_unknown=False` 
docstr_tok: ["Check","probability","calculation","where","`","use_unknown=False","`"]
code: def test_check_probs_with_unknown(self):
      """Check probability calculation where `use_unknown=False`"""  # noqa
      data = [
          "category 1",
          "category 3",
          "category 4",
          None,
          "category 3",
      ]
  
      enc = OneHotEncoder(use_unknown=False)
      enc.prepare(data)
  
      # Make data to represent random weights that do not sum to 1
      torch.manual_seed(1)
      wt_vec = torch.rand(size=(len(data), len(enc.map)))
  
      _, probs, _ = enc.decode_probabilities(wt_vec)
      self.assertTrue(np.all([np.isclose(sum(i), 1) for i in probs]))
  
file_path: lightwood/tests/unit_tests/encoder/categorical/test_onehot.py
imports: import unittest
  import torch
  from torch import Tensor
  import numpy
  from lightwood.encoder.categorical.onehot import OneHotEncoder
  from lightwood.helpers.constants import _UNCOMMON_WORD
label_desc: Use image node_id 3 for calling the TestOnehot obj's underlying member method code with example usage: obj.test_check_probs_with_unknown() without return types