code: from __future__ import division
  
  import numpy as np
  import pytest
  from numpy.testing import assert_almost_equal
  
  from mla.metrics.base import check_data, validate_input
  from mla.metrics.metrics import get_metric
  
  
  def test_data_validation():
      with pytest.raises(ValueError):
          check_data([], 1)
  
      with pytest.raises(ValueError):
          check_data([1, 2, 3], [3, 2])
  
      a, b = check_data([1, 2, 3], [3, 2, 1])
  
      assert np.all(a == np.array([1, 2, 3]))
      assert np.all(b == np.array([3, 2, 1]))
  
  
  def metric(name):
      return validate_input(get_metric(name))
  
  
  def test_classification_error():
      f = metric('classification_error')
      assert f([1, 2, 3, 4], [1, 2, 3, 4]) == 0
      assert f([1, 2, 3, 4], [1, 2, 3, 5]) == 0.25
      assert f([1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 0]) == (1.0 / 6)
  
  
  def test_absolute_error():
      f = metric('absolute_error')
      assert f([3], [5]) == [2]
      assert f([-1], [-4]) == [3]
  
  
  def test_mean_absolute_error():
      f = metric('mean_absolute_error')
      assert f([1, 2, 3], [1, 2, 3]) == 0
      assert f([1, 2, 3], [3, 2, 1]) == 4 / 3
  
  
  def test_squared_error():
      f = metric('squared_error')
      assert f([1], [1]) == [0]
      assert f([3], [1]) == [4]
  
  
  def test_squared_log_error():
      f = metric('squared_log_error')
      assert f([1], [1]) == [0]
      assert f([3], [1]) == [np.log(2) ** 2]
      assert f([np.exp(2) - 1], [np.exp(1) - 1]) == [1.0]
  
  
  def test_mean_squared_log_error():
      f = metric('mean_squared_log_error')
      assert f([1, 2, 3], [1, 2, 3]) == 0
      assert f([1, 2, 3, np.exp(1) - 1], [1, 2, 3, np.exp(2) - 1]) == 0.25
  
  
  def test_root_mean_squared_log_error():
      f = metric('root_mean_squared_log_error')
      assert f([1, 2, 3], [1, 2, 3]) == 0
      assert f([1, 2, 3, np.exp(1) - 1], [1, 2, 3, np.exp(2) - 1]) == 0.5
  
  
  def test_mean_squared_error():
      f = metric('mean_squared_error')
      assert f([1, 2, 3], [1, 2, 3]) == 0
      assert f(range(1, 5), [1, 2, 3, 6]) == 1
  
  
  def test_root_mean_squared_error():
      f = metric('root_mean_squared_error')
      assert f([1, 2, 3], [1, 2, 3]) == 0
      assert f(range(1, 5), [1, 2, 3, 5]) == 0.5
  
  
  def test_multiclass_logloss():
      f = metric('logloss')
      assert_almost_equal(f([1], [1]), 0)
      assert_almost_equal(f([1, 1], [1, 1]), 0)
      assert_almost_equal(f([1], [0.5]), -np.log(0.5))
  
apis: numpy.array numpy.log numpy.exp