Add unit test MMD univariate data

Author: jaime-cespedes-sisniega

frouros/tests/unit/detectors/__init__.py

@@ -0,0 +1 @@
+"""Detectors test init."""

frouros/tests/unit/detectors/data_drift/__init__.py

@@ -0,0 +1 @@
+"""Data drift detectors test init."""

frouros/tests/unit/detectors/data_drift/batch/__init__.py

@@ -0,0 +1 @@
+"""Batch data drift detectors test init."""

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

@@ -0,0 +1 @@
+"""Distance based batch data drift detectors test init."""

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

@@ -0,0 +1,49 @@
+"""Test MMD."""
+
+from functools import partial
+from typing import Tuple
+
+import numpy as np  # type: ignore
+import pytest  # type: ignore
+
+from frouros.detectors.data_drift import MMD
+from frouros.utils.kernels import rbf_kernel
+
+
+@pytest.mark.parametrize(
+    "distribution_p, distribution_q, expected_distance",
+    [
+        ((0, 1, 100), (0, 1, 100), 0.00052755),  # (mean, std, size)
+        ((0, 1, 100), (0, 1, 10), -0.03200193),
+        ((0, 1, 10), (0, 1, 100), 0.07154671),
+        ((2, 1, 100), (0, 1, 100), 0.43377622),
+        ((2, 1, 100), (0, 1, 10), 0.23051378),
+        ((2, 1, 10), (0, 1, 100), 0.62530767),
+    ],
+)
+def test_mmd_batch_univariate(
+    distribution_p: Tuple[float, float, int],
+    distribution_q: Tuple[float, float, int],
+    expected_distance: float,
+) -> None:
+    """Test MMD batch with univariate data.
+
+    :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 expected_distance: expected distance value
+    :type expected_distance: float
+    """
+    np.random.seed(seed=31)
+    X_ref = np.random.normal(*distribution_p)  # noqa: N806
+    X_test = np.random.normal(*distribution_q)  # noqa: N806
+
+    detector = MMD(
+        kernel=partial(rbf_kernel, sigma=0.5),
+    )
+    _ = detector.fit(X=X_ref)
+
+    result = detector.compare(X=X_test)[0]
+
+    assert np.isclose(result.distance, expected_distance)