def accuracy(global_predictions, local_predictions):
global_predictions[global_predictions >= 0.5] = 1
global_predictions[global_predictions < 0.5] = 0
local_predictions[local_predictions >= 0.5] = 1
local_predictions[local_predictions < 0.5] = 0
confusion_matrix = fumt.get_confusion_matrix(global_predictions,
local_predictions,
labels=[0, 1])
accuracy = fummet.accuracy(confusion_matrix)
return accuracy
def test_get_confusion_matrix():
"""
Tests :func:`fatf.utils.metrics.tools.get_confusion_matrix` function.
"""
# [[1, 1, 1],
# [1, 2, 1],
# [1, 1, 1]]
ground_truth = np.array(['a', 'b', 'b', 'b', 'a', 'a', 'b', 'c', 'c', 'c'])
predictions = np.array(['b', 'a', 'b', 'c', 'a', 'c', 'b', 'a', 'c', 'b'])
# [[3, 11],
# [7, 5 ]]
ground_truth_bin = np.array([
'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'a', 'a', 'a', 'a', 'a', 'a',
'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'
])
predictions_bin = np.array([
'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b',
'b', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a'
])
cmx = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
cmx_bin = np.array([[3, 11], [7, 5]])
cmx_bb = np.array([[1, 1, 0, 1], [1, 2, 0, 1], [0, 0, 0, 0], [1, 1, 0, 1]])
# Default labeling
cma = fumt.get_confusion_matrix(ground_truth, predictions)
assert np.array_equal(cmx, cma)
cma = fumt.get_confusion_matrix(ground_truth_bin, predictions_bin)
assert np.array_equal(cmx_bin, cma)
# Custom non-existing labeling
with pytest.warns(UserWarning) as w:
cma = fumt.get_confusion_matrix(ground_truth, predictions,
['a', 'b', 'bb', 'c'])
assert len(w) == 1
assert str(w[0].message) == USER_WARNING.format("{'bb'}")
assert np.array_equal(cmx_bb, cma)
def accuracy_prob(global_predictions,
local_predictions,
global_proba=True,
local_proba=True):
if global_proba:
global_predictions = np.argmax(global_predictions, axis=1)
if local_proba:
local_predictions = np.argmax(local_predictions, axis=1)
confusion_matrix = fumt.get_confusion_matrix(global_predictions,
local_predictions,
labels=[0, 1, 2])
accuracy = fummet.accuracy(confusion_matrix)
return accuracy
def test_get_confusion_matrix_errors():
"""
Tests :func:`fatf.utils.metrics.tools.get_confusion_matrix` errors.
"""
incorrect_shape_error_gt = ('The ground truth vector has to be '
'1-dimensional numpy array.')
incorrect_shape_error_pred = ('The predictions vector has to be '
'1-dimensional numpy array.')
incorrect_shape_error_gtp = ('Both the ground truth and the predictions '
'vectors have to have the same length.')
value_error_labels_empty = 'The labels list cannot be empty.'
value_error_labels_duplicates = 'The labels list contains duplicates.'
value_error_labels_missing = ('The following labels are present in the '
'input arrays but were not given in the '
'labels parameter: {}.')
type_error_labels = 'The labels parameter has to either a list or None.'
two_d_array = np.array([[1, 2], [3, 4]])
one_d_array_4 = np.array([1, 2, 3, 4])
one_d_array_5 = np.array([1, 2, 3, 4, 5])
cma_true = np.array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 1, 0], [0, 0, 0, 0, 0]])
with pytest.raises(IncorrectShapeError) as exi:
fumt.get_confusion_matrix(two_d_array, two_d_array)
assert str(exi.value) == incorrect_shape_error_gt
#
with pytest.raises(IncorrectShapeError) as exi:
fumt.get_confusion_matrix(one_d_array_4, two_d_array)
assert str(exi.value) == incorrect_shape_error_pred
#
with pytest.raises(IncorrectShapeError) as exi:
fumt.get_confusion_matrix(one_d_array_4, one_d_array_5)
assert str(exi.value) == incorrect_shape_error_gtp
with pytest.raises(TypeError) as exi:
fumt.get_confusion_matrix(one_d_array_4, one_d_array_4, 'a')
assert str(exi.value) == type_error_labels
#
with pytest.raises(ValueError) as exi:
fumt.get_confusion_matrix(one_d_array_4, one_d_array_4, [])
assert str(exi.value) == value_error_labels_empty
#
with pytest.raises(ValueError) as exi:
fumt.get_confusion_matrix(one_d_array_4, one_d_array_4, [2, 3, 2])
assert str(exi.value) == value_error_labels_duplicates
#
with pytest.raises(ValueError) as exi:
fumt.get_confusion_matrix(one_d_array_4, one_d_array_4, [2, 4, 3])
assert str(exi.value) == value_error_labels_missing.format('{1}')
with pytest.warns(UserWarning) as w:
cma = fumt.get_confusion_matrix(one_d_array_4, one_d_array_4,
[1, 2, 3, 4, 5])
assert len(w) == 1
assert str(w[0].message) == USER_WARNING.format('{5}')
assert np.array_equal(cma, cma_true)