Added tests for whole-series ROCKAD

Author: pattplatt

aeon/anomaly_detection/whole_series/tests/test_rockad.py

@@ -0,0 +1,67 @@
+"""Tests for the ROCKAD anomaly detector."""
+
+import numpy as np
+import pytest
+from sklearn.utils import check_random_state
+
+from aeon.anomaly_detection.whole_series import ROCKAD
+
+
+def test_rockad_univariate():
+    """Test ROCKAD univariate output."""
+    rng = check_random_state(seed=2)
+    train_series = rng.normal(loc=0.0, scale=1.0, size=(10, 100))
+    test_series = rng.normal(loc=0.0, scale=1.0, size=(5, 100))
+
+    test_series[0][50:58] -= 5
+
+    ad = ROCKAD(n_estimators=100, n_kernels=10, n_neighbors=9)
+
+    ad.fit(train_series)
+    pred = ad.predict(test_series)
+
+    assert pred.shape == (5,)
+    assert pred.dtype == np.float64
+    assert 0 <= np.argmax(pred) <= 1
+
+
+def test_rockad_multivariate():
+    """Test ROCKAD multivariate output."""
+    rng = check_random_state(seed=2)
+    train_series = rng.normal(loc=0.0, scale=1.0, size=(10, 3, 100))
+    test_series = rng.normal(loc=0.0, scale=1.0, size=(5, 3, 100))
+
+    test_series[0][0][50:58] -= 5
+
+    ad = ROCKAD(n_estimators=1000, n_kernels=100, n_neighbors=9)
+
+    ad.fit(train_series)
+    pred = ad.predict(test_series)
+
+    assert pred.shape == (5,)
+    assert pred.dtype == np.float64
+    assert 0 <= np.argmax(pred) <= 1
+
+
+def test_rockad_incorrect_input():
+    """Test ROCKAD with invalid inputs."""
+    rng = check_random_state(seed=2)
+    series = rng.normal(size=(10, 5))
+
+    with pytest.warns(
+        UserWarning, match=r"Power Transform failed and thus has been disabled."
+    ):
+        ad = ROCKAD()
+        ad.fit(series)
+
+    train_series = rng.normal(loc=0.0, scale=1.0, size=(10, 100))
+    test_series = rng.normal(loc=0.0, scale=1.0, size=(3, 100))
+
+    with pytest.raises(
+        ValueError,
+        match="""Expected n_neighbors <= n_samples_fit, but n_neighbors = 100,
+        n_samples_fit = 10, n_samples = 3""",
+    ):
+        ad = ROCKAD(n_estimators=100, n_kernels=10, n_neighbors=100)
+        ad.fit(train_series)
+        ad.predict(test_series)