code: def test_accurate_results():
      """Test that the actual results are the expected ones."""
      X_features = csr_matrix((n_samples, 0), dtype=np.int64)
      vocabulary_ = {}
  
      weasel = WEASEL(
          word_size=4,
          n_bins=3,
          window_sizes=[5, 10],
          window_steps=None,
          anova=True,
          drop_sum=True,
          norm_mean=True,
          norm_std=True,
          strategy="entropy",
          chi2_threshold=2,
          alphabet=None,
      )
  
      for window_size, n_windows in zip([5, 10], [40, 20]):
          X_windowed = X.reshape(n_samples, n_windows, window_size)
          X_windowed = X_windowed.reshape(
              n_samples * n_windows, window_size
          )
  
          sfa = SymbolicFourierApproximation(
              n_coefs=4,
              drop_sum=True,
              anova=True,
              norm_mean=True,
              norm_std=True,
              n_bins=3,
              strategy="entropy",
              alphabet=None,
          )
          y_repeated = np.repeat(y, n_windows)
          X_sfa = sfa.fit_transform(X_windowed, y_repeated)
          X_word = np.asarray(
              [
                  "".join(X_sfa[i])
                  for i in range((n_samples * n_windows))
              ]
          )
          X_word = X_word.reshape(n_samples, n_windows)
          X_bow = np.asarray(
              [" ".join(X_word[i]) for i in range(n_samples)]
          )
  
          vectorizer = CountVectorizer(ngram_range=(1, 2))
          X_counts = vectorizer.fit_transform(X_bow)
          chi2_statistics, _ = chi2(X_counts, y)
          relevant_features = np.where(chi2_statistics > 2)[0]
          X_features = hstack(
              [X_features, X_counts[:, relevant_features]]
          )
  
          old_length_vocab = len(vocabulary_)
          vocabulary = {
              value: key
              for (key, value) in vectorizer.vocabulary_.items()
          }
          for i, idx in enumerate(relevant_features):
              vocabulary_[i + old_length_vocab] = (
                  str(window_size) + " " + vocabulary[idx]
              )
  
      arr_desired = X_features.toarray()
  
      # Accuracte results for fit followed by transform
      arr_actual_1 = weasel.fit_transform(X, y).toarray()
      np.testing.assert_allclose(
          arr_actual_1, arr_desired, atol=1e-5, rtol=0
      )
      assert weasel.vocabulary_ == vocabulary_
  
      # Accuracte results for fit_transform
      arr_actual_2 = weasel.fit(X, y).transform(X).toarray()
      np.testing.assert_allclose(
          arr_actual_2, arr_desired, atol=1e-5, rtol=0
      )
      assert weasel.vocabulary_ == vocabulary_
  
file_path: pyts/pyts/transformation/tests/test_weasel.py
imports: import numpy
  import pytest
  import re
  from scipy.sparse import csr_matrix, hstack
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.feature_selection import chi2
  from pyts.approximation import SymbolicFourierApproximation
  from pyts.transformation import WEASEL
label_desc: Use image node_id 4 for calling a global function with example usage: test_accurate_results() without return types