code: def test_y_log_transformer_select():
      """Tests logarithmic data transformer with selection."""
      current_dir = os.path.dirname(os.path.abspath(__file__))
      multitask_dataset = load_feat_multitask_data()
      dfe = pd.read_csv(
          os.path.join(current_dir, "assets/feat_multitask_example.csv")
      )
      tid = []
      tasklist = ["task0", "task3", "task4", "task5"]
      first_task = "task0"
      for task in tasklist:
          tiid = dfe.columns.get_loc(task) - dfe.columns.get_loc(
              first_task
          )
          tid = np.concatenate((tid, np.array([tiid])))
      tasks = tid.astype(int)
      log_transformer = dc.trans.LogTransformer(
          transform_y=True, tasks=tasks, dataset=multitask_dataset
      )
      X, y, w, ids = (
          multitask_dataset.X,
          multitask_dataset.y,
          multitask_dataset.w,
          multitask_dataset.ids,
      )
      multitask_dataset = log_transformer.transform(multitask_dataset)
      X_t, y_t, w_t, ids_t = (
          multitask_dataset.X,
          multitask_dataset.y,
          multitask_dataset.w,
          multitask_dataset.ids,
      )
  
      # Check ids are unchanged.
      for id_elt, id_t_elt in zip(ids, ids_t):
          assert id_elt == id_t_elt
      # Check X is unchanged since this is a y transformer
      np.testing.assert_allclose(X, X_t)
      # Check w is unchanged since this is a y transformer
      np.testing.assert_allclose(w, w_t)
      # Check y is now a logarithmic version of itself
      np.testing.assert_allclose(y_t[:, tasks], np.log(y[:, tasks] + 1))
  
      # Check that untransform does the right thing.
      np.testing.assert_allclose(log_transformer.untransform(y_t), y)
  
file_path: deepchem/deepchem/trans/tests/test_log_transform.py
imports: import os
  import deepchem
  import pandas
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: test_y_log_transformer_select() without return types