code: def test_methods_tsbf(X, y, params):
      """Test the supported methods of TSBF."""
      n_samples = X.shape[0]
      n_classes = np.unique(y).size
      n_estimators = params.get(
          "n_estimators", TSBF().get_params()["n_estimators"]
      )
      clf = TSBF(**params).fit(X, y)
      assert clf.apply(X).shape == (n_samples, n_estimators)
      assert clf.decision_path(X)[0].shape[0] == n_samples
      assert clf.decision_path(X)[1].shape == (n_estimators + 1,)
      assert clf.predict(X).shape == (n_samples,)
      assert clf.predict_proba(X).shape == (n_samples, n_classes)
      assert isinstance(clf.score(X, y), (float, np.floating))
  
file_path: pyts/pyts/classification/tests/test_tsbf.py
imports: import numpy
  import pytest
  import re
  from pyts.classification import TSBF
  from pyts.classification.tsbf import IntervalFeatureExtractor
  from pyts.classification.tsbf import extract_features, histogram
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 11 for calling a global function with example usage: test_methods_tsbf(X, y, params) without return types