Add unit test MMD precomputed

Author: jaime-cespedes-sisniega

frouros/tests/unit/detectors/data_drift/batch/distance_based/test_mmd.py

@@ -1,7 +1,7 @@
 """Test MMD."""
 
 from functools import partial
-from typing import Tuple
+from typing import Optional, Tuple
 
 import numpy as np  # type: ignore
 import pytest  # type: ignore
@@ -47,3 +47,58 @@ def test_mmd_batch_univariate(
     result = detector.compare(X=X_test)[0]
 
     assert np.isclose(result.distance, expected_distance)
+
+
+@pytest.mark.parametrize(
+    "distribution_p, distribution_q, chunk_size",
+    [
+        ((0, 1, 100), (0, 1, 100), None),  # (mean, std, size)
+        ((0, 1, 100), (0, 1, 100), 2),
+        ((0, 1, 100), (0, 1, 100), 10),
+        ((0, 1, 100), (0, 1, 10), None),
+        ((0, 1, 100), (0, 1, 10), 2),
+        ((0, 1, 100), (0, 1, 10), 10),
+        ((0, 1, 10), (0, 1, 100), None),
+        ((0, 1, 10), (0, 1, 100), 2),
+        ((0, 1, 10), (0, 1, 100), 10),
+    ],
+)
+def test_mmd_batch_precomputed_expected_k_xx(
+    distribution_p: Tuple[float, float, int],
+    distribution_q: Tuple[float, float, int],
+    chunk_size: Optional[int],
+) -> None:
+    """Test MMD batch with precomputed expected k_xx.
+
+    :param distribution_p: mean, std and size of samples from distribution p
+    :type distribution_p: Tuple[float, float, int]
+    :param distribution_q: mean, std and size of samples from distribution q
+    :type distribution_q: Tuple[float, float, int]
+    :param chunk_size: chunk size
+    :type chunk_size: Optional[int]
+    """
+    np.random.seed(seed=31)
+    X_ref = np.random.normal(*distribution_p)  # noqa: N806
+    X_test = np.random.normal(*distribution_q)  # noqa: N806
+
+    kernel = partial(rbf_kernel, sigma=0.5)
+
+    detector = MMD(
+        kernel=kernel,
+        chunk_size=chunk_size,
+    )
+    _ = detector.fit(X=X_ref)
+
+    # Computes mmd using precomputed expected k_xx
+    precomputed_distance = detector.compare(X=X_test)[0].distance
+
+    # Computes mmd from scratch
+    scratch_distance = MMD._mmd(
+        X=X_ref,
+        Y=X_test,
+        kernel=kernel,
+        chunk_size=chunk_size,
+    )
+
+    assert np.isclose(precomputed_distance, scratch_distance)
+