code: def test_digits_log_naive():
      model = FeatureBasedSelection(100, "log", optimizer="naive")
      model.fit(X_digits)
      assert_array_equal(model.ranking[:30], digits_log_ranking[:30])
      assert_array_equal(model.ranking[-30:], digits_log_ranking[-30:])
      assert_array_almost_equal(model.gains, digits_log_gains, 4)
      assert_array_almost_equal(model.subset, X_digits[model.ranking])
  
imports: import scipy
  import numpy
  from apricot import FeatureBasedSelection
  from apricot.optimizers import *
  from sklearn.datasets import load_digits
  from sklearn.metrics import pairwise_distances
  from numpy.testing import assert_almost_equal
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 1 for calling a global function with example usage: test_digits_log_naive() without return types