code: def test_limit_vocabulary(self):
      # Given
      vectorizer = TfidfVectorizer()
      dataset = get_empty_dataset("en")
  
      utterances = [
          text_to_utterance("5 55 6 66 666"),
          text_to_utterance("55 66"),
      ]
  
      voca = {"5": 0, "55": 1, "6": 2, "66": 3, "666": 4}
      kept_unigrams = ["5", "6", "666"]
      vectorizer.fit(utterances, dataset)
      self.assertDictEqual(voca, vectorizer.vocabulary)
      diag = vectorizer.idf_diag.copy()
  
      # When
      vectorizer.limit_vocabulary(kept_unigrams)
  
      # Then
      expected_voca = {"5": 0, "6": 1, "666": 2}
      self.assertDictEqual(expected_voca, vectorizer.vocabulary)
  
      expected_diag = diag[[voca[u] for u in kept_unigrams]].tolist()
      self.assertListEqual(expected_diag, vectorizer.idf_diag.tolist())
  
imports: from __future__ import unicode_literals
  import io
  from builtins import str, zip, range
  import numpy
  from checksumdir import dirhash
  from mock import patch, MagicMock
  from snips_nlu.common.io_utils import temp_dir
  from snips_nlu.common.utils import json_string
  from snips_nlu.constants import LANGUAGE_EN, STEMS, WORD_CLUSTERS, STOP_WORDS
  from snips_nlu.dataset import Dataset
  from snips_nlu.entity_parser import BuiltinEntityParser, CustomEntityParser
  from snips_nlu.entity_parser.custom_entity_parser_usage import CustomEntityParserUsage
  from snips_nlu.exceptions import _EmptyDatasetUtterancesError
  from snips_nlu.intent_classifier.featurizer import CooccurrenceVectorizer, Featurizer, TfidfVectorizer
  from snips_nlu.intent_classifier.log_reg_classifier_utils import text_to_utterance
  from snips_nlu.pipeline.configs import FeaturizerConfig
  from snips_nlu.pipeline.configs.intent_classifier import CooccurrenceVectorizerConfig, TfidfVectorizerConfig
  from snips_nlu.tests.utils import FixtureTest, get_empty_dataset, EntityParserMock
label_desc: Use image node_id 2 for calling the TestTfidfVectorizer obj's underlying member method code with example usage: obj.test_limit_vocabulary() without return types