code: def test_sparse2_minimum_fraction():
      """Test fit_transform a sparse matrix including all zeros slice with minimum_fraction=0.5."""
      expected = np.array(
          [[0, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 1]], dtype=float
      ).transpose()
      fit_then_transform(expected, sparse2, minimum_fraction=0.5)
      fit_then_transform_dense(expected, sparse2, minimum_fraction=0.5)
  
imports: import numpy
  import scipy.sparse
  from numpy.testing import assert_array_almost_equal
  from sklearn.datasets import load_iris, load_boston
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.model_selection import cross_val_score, KFold
  from nose.tools import assert_equal
  from tpot.builtins import OneHotEncoder, auto_select_categorical_features, _transform_selected
label_desc: Use image node_id 15 for calling a global function with example usage: test_sparse2_minimum_fraction() without return types