def test_precision_at_k_perfect_multiple_true():
"""Test precision_at_k where the probabilities are perfect and there
are multiple true labels for some samples"""
y_true = np.array([
[1, 1, 0],
[1, 0, 0],
[0, 1, 1],
[1, 0, 1],
[0, 1, 1],
])
y_prob = np.array([
[0.3, 0.7, 0.0],
[0.1, 0.0, 0.0],
[0.1, 0.5, 0.3],
[0.6, 0.2, 0.4],
[0.1, 0.2, 0.3],
])
# Check results for k=1
expected = 1.0
result = metriks.precision_at_k(y_true, y_prob, 1)
np.testing.assert_allclose(result, expected)
print(result)
# Check results for k=2
expected = 9.0 / 10.0
result = metriks.precision_at_k(y_true, y_prob, 2)
np.testing.assert_allclose(result, expected)
print(result)
def test_precision_at_k_wikipedia():
"""
Tests the wikipedia example.
https://en.wikipedia.org/wiki/Mean_reciprocal_rank
"""
# Flag indicating which is actually relevant
y_true = np.array([
[0, 0, 1],
[0, 1, 0],
[1, 0, 0],
])
# The predicted probability
y_prob = np.array([
[0.4, 0.6, 0.3],
[0.1, 0.2, 0.9],
[0.9, 0.6, 0.3],
])
# Check results
expected = 2.0 / 6.0
result = metriks.precision_at_k(y_true, y_prob, 2)
np.testing.assert_allclose(result, expected)
print(result)
def test_precision_at_k_with_errors():
"""Test precision_at_k where there are errors in the probabilities"""
y_true = np.array([
[1, 1, 0],
[1, 0, 1],
[1, 1, 0],
[1, 0, 1],
[0, 1, 0],
[0, 1, 1],
[0, 1, 1],
[1, 0, 1],
[1, 1, 1],
])
y_prob = np.array([
[0.7, 0.6, 0.3],
[0.9, 0.2, 0.1], # 3rd probability is an error
[0.7, 0.8, 0.9], # 3rd probability is an error
[0.9, 0.8, 0.3], # 2nd and 3rd probability are swapped
[0.4, 0.6, 0.3],
[0.1, 0.6, 0.9],
[0.1, 0.6, 0.9],
[0.1, 0.6, 0.5],
[0.9, 0.8, 0.7],
])
# Check results
expected = 18.0 / 27.0
result = metriks.precision_at_k(y_true, y_prob, 3)
np.testing.assert_allclose(result, expected)
print(result)
def test_precision_at_k_zeros():
"""Test precision_at_k where there is a sample of all zeros"""
y_true = np.array([[0, 0, 1, 1], [1, 1, 1, 0], [0, 0, 0, 0]])
y_prob = np.array([[0.1, 0.4, 0.35, 0.8], [0.3, 0.2, 0.7, 0.8],
[0.1, 0.2, 0.3, 0.4]])
# Check results
expected = 4.0 / 9.0
result = metriks.precision_at_k(y_true, y_prob, 3)
np.testing.assert_allclose(result, expected)
print(result)
def test_precision_at_k_few_zeros():
"""Test precision_at_k where there are very few zeros"""
y_true = np.array([[0, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 0]])
y_prob = np.array([[0.1, 0.4, 0.35, 0.8, 0.9], [0.3, 0.2, 0.7, 0.8, 0.6],
[0.1, 0.2, 0.3, 0.4, 0.5]])
# Check results
expected = 5.0 / 6.0
result = metriks.precision_at_k(y_true, y_prob, 2)
np.testing.assert_allclose(result, expected)
print(result)
def test_precision_at_k_perfect():
"""Test precision_at_k where the probabilities are perfect"""
y_true = np.array([
[0, 1, 0],
[1, 0, 0],
[0, 1, 0],
[1, 0, 0],
[0, 0, 1],
])
y_prob = np.array([
[0.3, 0.7, 0.0],
[0.1, 0.0, 0.0],
[0.1, 0.5, 0.3],
[0.6, 0.2, 0.4],
[0.1, 0.2, 0.3],
])
# Check results
expected = 1.0
result = metriks.precision_at_k(y_true, y_prob, 1)
np.testing.assert_allclose(result, expected)
print(result)