def test_float_fail():
    y = np.array([1, 2], dtype=np.int64)
    reg = _BaseRegressor(print_progress=0, random_seed=1)
    assert_raises(AttributeError,
                  'y must be a float array.\nFound int64',
                  reg._check_target_array,
                  y)

def test_fit_1():
    X = np.array([[1], [2], [3]])
    est = _BaseSupervisedEstimator(print_progress=0, random_seed=1)
    assert_raises(TypeError,
                  "fit() missing 1 required positional argument: 'y'",
                  est.fit,
                  X)

def test_check_labels_positive_notok():
    y = np.array([1, 1, -1])
    cl = BlankClassifier(print_progress=0, random_seed=1)
    assert_raises(AttributeError,
                  'y array must not contain negative labels.\nFound [-1  1]',
                  cl._check_target_array,
                  y)

def test_fit_1():
    X = np.array([[1], [2], [3]])
    est = BlankClassifier(print_progress=0, random_seed=1)
    assert_raises(TypeError,
                  "fit() missing 1 required positional argument: 'y'",
                  est.fit,
                  X)

def test_unequal_length_X():
    assert_raises(ValueError,
                  ('y and X must contain the same number of samples. '
                   'Got y: 4, X: 3'),
                  check_Xy,
                  X[1:],
                  y)

def test_check_array_1():
    X = np.array([1, 2, 3])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    assert_raises(ValueError,
                  'X must be a 2D array. Try X[:, numpy.newaxis]',
                  est._check_arrays,
                  X)

def test_check_array_1():
    X = np.array([1, 2, 3])
    est = BlankModel()
    assert_raises(ValueError,
                  'X must be a 2D array. Try X[:, numpy.newaxis]',
                  est._check_arrays,
                  X)

def test_check_array_1():
    X = np.array([1, 2, 3])
    cl = _BaseCluster(print_progress=0, random_seed=1)
    assert_raises(ValueError,
                  'X must be a 2D array. Try X[:, numpy.newaxis]',
                  cl._check_array,
                  X)

def test_unequal_length_y():
    assert_raises(ValueError,
                  ('y and X must contain the same number of samples. '
                   'Got y: 3, X: 4'),
                  check_Xy,
                  X,
                  y[1:])

def test_filler_feature_values_fail():
    sr.fit(X, y)
    assert_raises(ValueError,
                  'Filler values must be provided when '
                  'X has more than 2 training features.',
                  plot_decision_regions,
                  X, y, sr)

def test_fit_1():
    X = np.array([[1], [2], [3]])
    est = BlankRegressor()
    assert_raises(TypeError,
                  "fit() missing 1 required positional argument: 'y'",
                  est.fit,
                  X)

def test_check_labels_interger_notok():
    y = np.array([1., 2.], dtype=np.float64)
    cl = BlankClassifier(print_progress=0, random_seed=1)
    assert_raises(AttributeError,
                  'y must be an integer array.\nFound float64',
                  cl._check_target_array,
                  y)

def test_float_fail():
    y = np.array([1, 2], dtype=np.int64)
    reg = BlankRegressor()
    assert_raises(AttributeError,
                  'y must be a float array.\nFound int64',
                  reg._check_target_array,
                  y)

def test_y_int_ary():
    sr.fit(X[:, :2], y)
    assert_raises(ValueError,
                  'y must be an integer array. Found float64. '
                  'Try passing the array as y.astype(np.integer)',
                  plot_decision_regions,
                  X[:, :2], y.astype(np.float), sr)

def test_check_array_2():
    X = list([[1], [2], [3]])
    cl = _BaseCluster(print_progress=0, random_seed=1)

    assert_raises(ValueError,
                  'X must be a numpy array',
                  cl._check_array,
                  X)

def test_format_kwarg_dictionaries_user_kwargs_invalid_type():
    invalid_kwargs = 'not a dictionary'
    message = ('d must be of type dict or None, but got '
               '{} instead'.format(type(invalid_kwargs)))
    assert_raises(TypeError,
                  message,
                  format_kwarg_dictionaries,
                  user_kwargs=invalid_kwargs)

def test_input_array_1d():
    t = np.array([[1, 2], [3, 4]])
    assert_raises(ValueError,
                  'One or more input arrays are not 1-dimensional.',
                  mcnemar_table,
                  t,
                  t,
                  t)

def test_check_array_2():
    X = list([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)

    assert_raises(ValueError,
                  'X must be a numpy array',
                  est._check_arrays,
                  X)

def test_predict_1():
    X = np.array([[1], [2], [3]])
    cl = _BaseCluster(print_progress=0, random_seed=1)

    assert_raises(AttributeError,
                  'Model is not fitted, yet.',
                  cl.predict,
                  X)

def test_check_labels_not_ok_1():
    y = np.array([1, 3, 2])
    cl = BlankClassifier(print_progress=0, random_seed=1)
    assert_raises(AttributeError,
                  'Labels not in {(1, 2), (0, 1)}.\nFound (1, 2, 3)',
                  cl._check_target_array,
                  y,
                  {(0, 1), (1, 2)})