content: import numpy as np
  
  from sklearn.utils.class_weight import compute_class_weight
  from sklearn.utils.class_weight import compute_sample_weight
  
  from sklearn.utils.testing import assert_array_almost_equal
  from sklearn.utils.testing import assert_almost_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_true
  from sklearn.utils.testing import assert_equal
  
  
  def test_compute_class_weight():
      # Test (and demo) compute_class_weight.
      y = np.asarray([2, 2, 2, 3, 3, 4])
      classes = np.unique(y)
      cw = compute_class_weight("auto", classes, y)
      assert_almost_equal(cw.sum(), classes.shape)
      assert_true(cw[0] < cw[1] < cw[2])
  
  
  def test_compute_class_weight_not_present():
      # Raise error when y does not contain all class labels
      classes = np.arange(4)
      y = np.asarray([0, 0, 0, 1, 1, 2])
      assert_raises(ValueError, compute_class_weight, "auto", classes, y)
  
  
  def test_compute_class_weight_auto_negative():
      # Test compute_class_weight when labels are negative
      # Test with balanced class labels.
      classes = np.array([-2, -1, 0])
      y = np.asarray([-1, -1, 0, 0, -2, -2])
      cw = compute_class_weight("auto", classes, y)
      assert_almost_equal(cw.sum(), classes.shape)
      assert_equal(len(cw), len(classes))
      assert_array_almost_equal(cw, np.array([1., 1., 1.]))
  
      # Test with unbalanced class labels.
      y = np.asarray([-1, 0, 0, -2, -2, -2])
      cw = compute_class_weight("auto", classes, y)
      assert_almost_equal(cw.sum(), classes.shape)
      assert_equal(len(cw), len(classes))
      assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3)
  
  
  def test_compute_class_weight_auto_unordered():
      # Test compute_class_weight when classes are unordered
      classes = np.array([1, 0, 3])
      y = np.asarray([1, 0, 0, 3, 3, 3])
      cw = compute_class_weight("auto", classes, y)
      assert_almost_equal(cw.sum(), classes.shape)
      assert_equal(len(cw), len(classes))
      assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3)
  
  
  def test_compute_sample_weight():
      # Test (and demo) compute_sample_weight.
      # Test with balanced classes
      y = np.asarray([1, 1, 1, 2, 2, 2])
      sample_weight = compute_sample_weight("auto", y)
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
  
      # Test with user-defined weights
      sample_weight = compute_sample_weight({1: 2, 2: 1}, y)
      assert_array_almost_equal(sample_weight, [2., 2., 2., 1., 1., 1.])
  
      # Test with column vector of balanced classes
      y = np.asarray([[1], [1], [1], [2], [2], [2]])
      sample_weight = compute_sample_weight("auto", y)
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
  
      # Test with unbalanced classes
      y = np.asarray([1, 1, 1, 2, 2, 2, 3])
      sample_weight = compute_sample_weight("auto", y)
      expected = np.asarray([.6, .6, .6, .6, .6, .6, 1.8])
      assert_array_almost_equal(sample_weight, expected)
  
      # Test with `None` weights
      sample_weight = compute_sample_weight(None, y)
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 1.])
  
      # Test with multi-output of balanced classes
      y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
      sample_weight = compute_sample_weight("auto", y)
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
  
      # Test with multi-output with user-defined weights
      y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
      sample_weight = compute_sample_weight([{1: 2, 2: 1}, {0: 1, 1: 2}], y)
      assert_array_almost_equal(sample_weight, [2., 2., 2., 2., 2., 2.])
  
      # Test with multi-output of unbalanced classes
      y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [3, -1]])
      sample_weight = compute_sample_weight("auto", y)
      assert_array_almost_equal(sample_weight, expected ** 2)
  
  
  def test_compute_sample_weight_with_subsample():
      # Test compute_sample_weight with subsamples specified.
      # Test with balanced classes and all samples present
      y = np.asarray([1, 1, 1, 2, 2, 2])
      sample_weight = compute_sample_weight("auto", y, range(6))
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
  
      # Test with column vector of balanced classes and all samples present
      y = np.asarray([[1], [1], [1], [2], [2], [2]])
      sample_weight = compute_sample_weight("auto", y, range(6))
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
  
      # Test with a subsample
      y = np.asarray([1, 1, 1, 2, 2, 2])
      sample_weight = compute_sample_weight("auto", y, range(4))
      assert_array_almost_equal(sample_weight, [.5, .5, .5, 1.5, 1.5, 1.5])
  
      # Test with a bootstrap subsample
      y = np.asarray([1, 1, 1, 2, 2, 2])
      sample_weight = compute_sample_weight("auto", y, [0, 1, 1, 2, 2, 3])
      expected = np.asarray([1/3., 1/3., 1/3., 5/3., 5/3., 5/3.])
      assert_array_almost_equal(sample_weight, expected)
  
      # Test with a bootstrap subsample for multi-output
      y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
      sample_weight = compute_sample_weight("auto", y, [0, 1, 1, 2, 2, 3])
      assert_array_almost_equal(sample_weight, expected ** 2)
  
      # Test with a missing class
      y = np.asarray([1, 1, 1, 2, 2, 2, 3])
      sample_weight = compute_sample_weight("auto", y, range(6))
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.])
  
      # Test with a missing class for multi-output
      y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [2, 2]])
      sample_weight = compute_sample_weight("auto", y, range(6))
      assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.])
  
  
  def test_compute_sample_weight_errors():
      # Test compute_sample_weight raises errors expected.
      # Invalid preset string
      y = np.asarray([1, 1, 1, 2, 2, 2])
      y_ = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
      assert_raises(ValueError, compute_sample_weight, "ni", y)
      assert_raises(ValueError, compute_sample_weight, "ni", y, range(4))
      assert_raises(ValueError, compute_sample_weight, "ni", y_)
      assert_raises(ValueError, compute_sample_weight, "ni", y_, range(4))
  
      # Not "auto" for subsample
      assert_raises(ValueError,
                    compute_sample_weight, {1: 2, 2: 1}, y, range(4))
  
      # Not a list or preset for multi-output
      assert_raises(ValueError, compute_sample_weight, {1: 2, 2: 1}, y_)
  
      # Incorrect length list for multi-output
      assert_raises(ValueError, compute_sample_weight, [{1: 2, 2: 1}], y_)
  