code: def test_eval_measures():
      # mainly regression tests
      x = np.arange(20).reshape(4, 5)
      y = np.ones((4, 5))
  
      assert_equal(iqr(x, y), 5 * np.ones(5))
      assert_equal(iqr(x, y, axis=1), 2 * np.ones(4))
      assert_equal(iqr(x, y, axis=None), 9)
  
      assert_equal(
          mse(x, y), np.array([73.5, 87.5, 103.5, 121.5, 141.5])
      )
      assert_equal(
          mse(x, y, axis=1), np.array([3.0, 38.0, 123.0, 258.0])
      )
  
      assert_almost_equal(
          rmse(x, y),
          np.array(
              [
                  8.5732141,
                  9.35414347,
                  10.17349497,
                  11.02270384,
                  11.89537725,
              ]
          ),
      )
      assert_almost_equal(
          rmse(x, y, axis=1),
          np.array([1.73205081, 6.164414, 11.09053651, 16.0623784]),
      )
  
      err = x - y
      loc = np.where(x != 0)
      err[loc] /= x[loc]
      err[np.where(x == 0)] = np.nan
      expected = np.sqrt(np.nanmean(err**2, 0) * 100)
      assert_almost_equal(rmspe(x, y), expected)
      err[np.where(np.isnan(err))] = 0.0
      expected = np.sqrt(np.nanmean(err**2, 0) * 100)
      assert_almost_equal(rmspe(x, y, zeros=0), expected)
  
      assert_equal(
          maxabs(x, y), np.array([14.0, 15.0, 16.0, 17.0, 18.0])
      )
      assert_equal(
          maxabs(x, y, axis=1), np.array([3.0, 8.0, 13.0, 18.0])
      )
  
      assert_equal(meanabs(x, y), np.array([7.0, 7.5, 8.5, 9.5, 10.5]))
      assert_equal(
          meanabs(x, y, axis=1), np.array([1.4, 6.0, 11.0, 16.0])
      )
      assert_equal(
          meanabs(x, y, axis=0), np.array([7.0, 7.5, 8.5, 9.5, 10.5])
      )
  
      assert_equal(
          medianabs(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5])
      )
      assert_equal(
          medianabs(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0])
      )
  
      assert_equal(bias(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5]))
      assert_equal(bias(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0]))
  
      assert_equal(
          medianbias(x, y), np.array([6.5, 7.5, 8.5, 9.5, 10.5])
      )
      assert_equal(
          medianbias(x, y, axis=1), np.array([1.0, 6.0, 11.0, 16.0])
      )
  
      assert_equal(
          vare(x, y), np.array([31.25, 31.25, 31.25, 31.25, 31.25])
      )
      assert_equal(vare(x, y, axis=1), np.array([2.0, 2.0, 2.0, 2.0]))
  
file_path: statsmodels/statsmodels/tools/tests/test_eval_measures.py
imports: import numpy
  from numpy.testing import assert_almost_equal, assert_equal
  import pytest
  from statsmodels.tools.eval_measures import aic, aic_sigma, aicc, aicc_sigma, bias, bic, bic_sigma, hqic, hqic_sigma, iqr, maxabs, meanabs, medianabs, medianbias, mse, rmse, rmspe, vare
label_desc: Use image node_id 1 for calling a global function with example usage: test_eval_measures() without return types