def test_y_arguments():
"Test exception is raised when user provides invalid vectors"
try:
scoring(y_target=[1, 2], y_predicted=[1])
assert False
except AttributeError:
assert True
def test_metric_argument():
"Test exception is raised when user provides invalid metric argument"
try:
scoring(y_target=[1], y_predicted=[1], metric='test')
assert False
except AttributeError:
assert True
def test_metric_argument():
"Test exception is raised when user provides invalid metric argument"
try:
scoring(y_target=[1], y_predicted=[1], metric='test')
assert False
except AttributeError:
assert True
def test_y_arguments():
"Test exception is raised when user provides invalid vectors"
try:
scoring(y_target=[1, 2], y_predicted=[1])
assert False
except AttributeError:
assert True
def test_binary():
"Test exception is raised if label is not binary in f1"
try:
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
scoring(y_target=y_targ, y_predicted=y_pred, metric='f1')
assert False
except AttributeError:
assert True
def test_binary():
"Test exception is raised if label is not binary in f1"
try:
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
scoring(y_target=y_targ, y_predicted=y_pred, metric='f1')
assert False
except AttributeError:
assert True
def _get_scores(target, predicted):
"""
Helper method to get the accuracy, recall, precision and f1 scores
:param target: a numpy array containing the target values
:param predicted: a numpy array containing the predicted values
:return: a numpy array containing the scores
"""
recall = scoring(target, predicted, metric="recall")
precision = scoring(target, predicted, metric="precision")
accuracy = scoring(target, predicted, metric="accuracy")
f_score = scoring(target, predicted, metric="f1")
return [recall, precision, accuracy, f_score]
def test_avg_perclass_error():
y_targ = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])
y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='per-class error')
assert round(res, 3) == 0.333, res
def test_avg_perclass_accuracy():
y_targ = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])
y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='per-class accuracy')
assert round(res, 3) == 0.667, res
def test_matthews_corr_coef():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='matthews_corr_coef')
assert round(res, 3) == 0.258, res
def test_falsepositiverate():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='false_positive_rate')
assert round(res, 3) == 0.333, res
def test_falsepositiverate():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='false_positive_rate')
assert round(res, 3) == 0.333, res
def test_avg_perclass_error():
y_targ = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])
y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='average per-class error')
assert round(res, 3) == 0.333, res
def test_matthews_corr_coef():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='matthews_corr_coef')
assert round(res, 3) == 0.258, res
def test_balanced_accuracy():
y_targ = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])
y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='balanced accuracy')
assert round(res, 3) == 0.578, res
def test_avg_perclass_accuracy():
y_targ = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])
y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])
res = scoring(y_target=y_targ,
y_predicted=y_pred,
metric='average per-class accuracy')
assert round(res, 3) == 0.667, res
def test_precision():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='precision')
assert round(res, 3) == 0.75, res
def test_f1():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='f1')
assert round(res, 3) == 0.667, res
def test_error():
"Test error metric"
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='error')
assert res == 0.25
def test_accuracy():
"Test accuracy metric"
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='accuracy')
assert res == 0.75
def test_accuracy():
"Test accuracy metric"
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='accuracy')
assert res == 0.75
def test_f1():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='f1')
assert round(res, 3) == 0.667, res
def test_error():
"Test error metric"
y_targ = [1, 1, 1, 0, 0, 2, 0, 3]
y_pred = [1, 0, 1, 0, 0, 2, 1, 3]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='error')
assert res == 0.25
def test_precision():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='precision')
assert round(res, 3) == 0.75, res
def test_specificity():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='specificity')
assert round(res, 3) == 0.667, res
def test_specificity():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='specificity')
assert round(res, 3) == 0.667, res
def test_recall():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='recall')
assert round(res, 3) == 0.6, res
def test_sensitivity():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='sensitivity')
assert round(res, 3) == 0.6, res
def test_recall():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='recall')
assert round(res, 3) == 0.6, res
def test_sensitivity():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric='sensitivity')
assert round(res, 3) == 0.6, res
def test_truepositiverate():
y_targ = [1, 1, 1, 0, 0, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 0, 1, 1]
res = scoring(y_target=y_targ, y_predicted=y_pred, metric="true_positive_rate")
assert round(res, 3) == 0.6, res
