def test_confusion_matrix_normalize(normalize, expected_results, cluster):
client = Client(cluster)
y_test = da.from_array(cp.array([0, 1, 2] * 6))
y_pred = da.from_array(cp.array(list(chain(*permutations([0, 1, 2])))))
cm = confusion_matrix(y_test, y_pred, normalize=normalize)
cp.testing.assert_allclose(cm, cp.array(expected_results))
client.close()
def test_confusion_matrix_binary(cluster, chunks):
client = Client(cluster)
y_true = da.from_array(cp.array([0, 1, 0, 1]), chunks=chunks)
y_pred = da.from_array(cp.array([1, 1, 1, 0]), chunks=chunks)
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
ref = cp.array([0, 2, 1, 1])
cp.testing.assert_array_equal(ref, cp.array([tn, fp, fn, tp]))
client.close()
def test_confusion_matrix(cluster, chunks):
client = Client(cluster)
y_true = da.from_array(cp.array([2, 0, 2, 2, 0, 1]), chunks=chunks)
y_pred = da.from_array(cp.array([0, 0, 2, 2, 0, 2]), chunks=chunks)
cm = confusion_matrix(y_true, y_pred)
ref = cp.array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
cp.testing.assert_array_equal(cm, ref)
client.close()
def test_confusion_matrix_multiclass_subset_labels(labels, client):
y_true, y_pred, np_y_true, np_y_pred = generate_random_labels(
lambda rng: rng.randint(0, 3, 10).astype(np.int32), as_cupy=True)
y_true, y_pred = da.from_array(y_true), da.from_array(y_pred)
ref = sk_confusion_matrix(np_y_true, np_y_pred, labels=labels)
labels = cp.array(labels, dtype=np.int32)
cm = confusion_matrix(y_true, y_pred, labels=labels)
cp.testing.assert_array_almost_equal(ref, cm, decimal=4)
def test_confusion_matrix_random(n_samples, dtype, problem_type, client):
upper_range = 2 if problem_type == 'binary' else 1000
y_true, y_pred, np_y_true, np_y_pred = generate_random_labels(
lambda rng: rng.randint(0, upper_range, n_samples).astype(dtype),
as_cupy=True)
y_true, y_pred = da.from_array(y_true), da.from_array(y_pred)
cm = confusion_matrix(y_true, y_pred)
ref = sk_confusion_matrix(np_y_true, np_y_pred)
cp.testing.assert_array_almost_equal(ref, cm, decimal=4)
def test_confusion_matrix_random_weights(n_samples, dtype, weights_dtype,
client):
y_true, y_pred, np_y_true, np_y_pred = generate_random_labels(
lambda rng: rng.randint(0, 10, n_samples).astype(dtype), as_cupy=True)
y_true, y_pred = da.from_array(y_true), da.from_array(y_pred)
if weights_dtype == 'int':
sample_weight = np.random.RandomState(0).randint(0, 10, n_samples)
else:
sample_weight = np.random.RandomState(0).rand(n_samples)
ref = sk_confusion_matrix(np_y_true,
np_y_pred,
sample_weight=sample_weight)
sample_weight = cp.array(sample_weight)
sample_weight = da.from_array(sample_weight)
cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
cp.testing.assert_array_almost_equal(ref, cm, decimal=4)