def average_precision_score(y_true, y_score):
"""
Compute average precision score using precision and recall computed from cuml.
"""
precision, recall, _ = precision_recall_curve(y_true, y_score)
# return step function integral
return -cp.sum(cp.diff(recall) * cp.array(precision)[:-1])
def test_precision_recall_curve_at_limits():
y_true = np.array([0., 0., 0.], dtype=np.float)
y_pred = np.array([0., 0.5, 1.], dtype=np.float)
err_msg = ("precision_recall_curve cannot be used when "
"y_true is all zero.")
with pytest.raises(ValueError, match=err_msg):
precision_recall_curve(y_true, y_pred)
y_true = np.array([0., 0.5, 1.0], dtype=np.float)
y_pred = np.array([0., 0.5, 1.], dtype=np.float)
err_msg = ("Continuous format of y_true " "is not supported.")
with pytest.raises(ValueError, match=err_msg):
precision_recall_curve(y_true, y_pred)
def plot_pr(label, y_scores):
ap = metrics(label, y_scores)[1]
precision, recall, _ = precision_recall_curve(label, y_scores)
plt.plot(recall, precision, label='AP = ' + str(np.round(ap, 2)))
plt.ylabel('Precision')
plt.xlabel('Recall')
plt.legend(loc='best')
plt.title('Area under PR curve')
def test_precision_recall_curve():
y_true = np.array([0, 0, 1, 1])
y_score = np.array([0.1, 0.4, 0.35, 0.8])
precision_using_sk, recall_using_sk, thresholds_using_sk = \
sklearn_precision_recall_curve(
y_true, y_score)
precision, recall, thresholds = precision_recall_curve(y_true, y_score)
assert array_equal(precision, precision_using_sk)
assert array_equal(recall, recall_using_sk)
assert array_equal(thresholds, thresholds_using_sk)
def test_precision_recall_curve_random(n_samples, dtype):
y_true, _, _, _ = generate_random_labels(
lambda rng: rng.randint(0, 2, n_samples).astype(dtype))
y_score, _, _, _ = generate_random_labels(
lambda rng: rng.randint(0, 1000, n_samples).astype(dtype))
precision_using_sk, recall_using_sk, thresholds_using_sk = \
sklearn_precision_recall_curve(
y_true, y_score)
precision, recall, thresholds = precision_recall_curve(y_true, y_score)
assert array_equal(precision, precision_using_sk)
assert array_equal(recall, recall_using_sk)
assert array_equal(thresholds, thresholds_using_sk)
