code: def test_build(self):
      from sklearn.feature_extraction.text import CountVectorizer
  
      categories, docs = get_docs_categories_semiotic()
      idx_store = IndexStore()
      y = np.array([idx_store.getidx(c) for c in categories])
      count_vectorizer = CountVectorizer()
      X_counts = count_vectorizer.fit_transform(docs)
      term_doc_mat = TermDocMatrixFromScikit(
          X=X_counts,
          y=y,
          feature_vocabulary=count_vectorizer.vocabulary_,
          category_names=idx_store.values(),
      ).build()
      self.assertEqual(
          term_doc_mat.get_categories()[:2],
          ["hamlet", "jay-z/r. kelly"],
      )
      self.assertEqual(
          term_doc_mat.get_term_freq_df()
          .assign(score=term_doc_mat.get_scaled_f_scores("hamlet"))
          .sort_values(by="score", ascending=False)
          .index.tolist()[:5],
          ["that", "march", "did", "majesty", "sometimes"],
      )
  
imports: from unittest import TestCase
  import numpy
  from scattertext import TermDocMatrixFromScikit
  from scattertext.indexstore import IndexStore
  from scattertext.test.test_semioticSquare import get_docs_categories_semiotic
label_desc: Use image node_id 1 for calling the TestTermDocMatrixFromScikit obj's underlying member method code with example usage: obj.test_build() without return types