def test_select_more_scores_than_k_min(self, bandit_mock):
"""If min length is gt k_min, self.compute_rewards is used.
In this case, we expect the lower scores to be zeroed.
"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'])
bandit_mock.return_value = 'RF'
# Run
choice_scores = {
'DT': [0.7, 0.75, 0.73],
'RF': [0.8, 0.85, 0.83],
'SVM': [0.9, 0.95, 0.91],
}
best = selector.select(choice_scores)
# Assert
assert best == 'RF'
choice_rewards = {
'RF': [approx(0.02), approx(0.03)],
'SVM': [approx(0.04), approx(0.01)],
}
bandit_mock.assert_called_once_with(choice_rewards)
def test_select_less_scores_than_k_min(self, bandit_mock):
"""If min length is lt k_min, super().compute_rewards is used.
In this case, we expect the socres to be returned as they are.
"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'])
bandit_mock.return_value = 'RF'
# Run
choice_scores = {
'DT': [0.7, 0.75, 0.73],
'RF': [0.8],
'SVM': [0.9],
}
best = selector.select(choice_scores)
# Assert
assert best == 'RF'
choice_rewards = {
'RF': [1],
'SVM': [1],
}
bandit_mock.assert_called_once_with(choice_rewards)
def test_select_less_scores_than_k_min(self, bandit_mock):
"""If min length is lt k_min, super().compute_rewards is used.
In this case, we expect the socres to be returned as they are.
"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'])
bandit_mock.return_value = 'RF'
# Run
choice_scores = {
'DT': [0.7, 0.75, 0.73],
'RF': [0.8],
'SVM': [0.9],
}
best = selector.select(choice_scores)
# Assert
assert best == 'RF'
choice_rewards = {
'RF': [1],
'SVM': [1],
}
bandit_mock.assert_called_once_with(choice_rewards)
def test_select_more_scores_than_k_min(self, bandit_mock):
"""If min length is gt k_min, self.compute_rewards is used.
In this case, we expect the lower scores to be zeroed.
"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'])
bandit_mock.return_value = 'RF'
# Run
choice_scores = {
'DT': [0.7, 0.75, 0.73],
'RF': [0.8, 0.85, 0.83],
'SVM': [0.9, 0.95, 0.91],
}
best = selector.select(choice_scores)
# Assert
assert best == 'RF'
choice_rewards = {
'RF': [approx(0.02), approx(0.03)],
'SVM': [approx(0.04), approx(0.01)],
}
bandit_mock.assert_called_once_with(choice_rewards)
def test_compute_rewards_gt_k(self):
"""More scores than self.k: padding"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'], k=3)
# Run
scores = [0.1, 0.5, 0.6, 0.75, 0.8]
rewards = selector.compute_rewards(scores)
# Assert
np.testing.assert_allclose(rewards, [0.05, 0.15, 0.1, 0., 0.])
def test_compute_rewards_gt_k(self):
"""More scores than self.k: padding"""
# Set-up
selector = PureBestKVelocity(['RF', 'SVM'], k=3)
# Run
scores = [0.1, 0.5, 0.6, 0.75, 0.8]
rewards = selector.compute_rewards(scores)
# Assert
np.testing.assert_allclose(rewards, [0.05, 0.15, 0.1, 0., 0.])
def test___init__(self):
# Run
selector = PureBestKVelocity(['RF', 'SVM'], k=3)
# Assert
assert selector.choices == ['RF', 'SVM']
assert selector.k == 3