updated code as requested by moderators

aeon/anomaly_detection/_idk.py

@@ -1,8 +1,8 @@
 import numpy as np
 
 from aeon.anomaly_detection.base import BaseAnomalyDetector
-
-
+from aeon.utils.windowing import reverse_windowing
+from typing import Optional
 class IDK(BaseAnomalyDetector):
     """IDK² and s-IDK² anomaly detector.
 
@@ -78,7 +78,7 @@ def __init__(
         width: int = 1,
         t: int = 100,
         sliding: bool = False,
-        random_state: int = None,
+        random_state: Optional[int] = None,
     ) -> None:
         self.psi1 = psi1
         self.psi2 = psi2
@@ -175,9 +175,16 @@ def _idk_square_sliding(self, X, rng):
     def _predict(self, X):
         rng = np.random.default_rng(self.random_state)
         if self.sliding:
-            return self._idk_square_sliding(X, rng)
-        return self._idk_t(X, rng)
-
+            sliding_output = self._idk_square_sliding(X, rng)
+            reversed_output = reverse_windowing(
+                y=sliding_output,
+                window_size=self.width,
+                stride=1,
+                reduction=np.nanmean,
+            )
+            return reversed_output
+        else:
+            return self._idk_t(X, rng)
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
@@ -199,4 +206,4 @@ def _get_test_params(cls, parameter_set="default"):
             "psi1": 8,
             "psi2": 2,
             "width": 1,
-        }
+        }

aeon/anomaly_detection/tests/test_idk.py

@@ -1,41 +1,56 @@
 """Tests for the IDK Class."""
 
 import numpy as np
-
+from numpy.testing import assert_allclose
 from aeon.anomaly_detection import IDK
 
+def test_idk_univariate_basic():
+    """Test IDK on basic univariate data."""
+    rng = np.random.default_rng(seed=2)
+    series = rng.normal(size=(100,))
+    series[50:58] -= 5
+
+    ad = IDK(psi1=8, psi2=2, width=1, random_state=2)
+    pred = ad.fit_predict(series)
+
+    assert pred.shape == (100,)
+    assert pred.dtype == np.float64
+    assert 50 <= np.argmax(pred) <= 58
 
-def test_idk_univariate():
-    """Test IDK on univariate data."""
+def test_idk_univariate_sliding():
+    """Test IDK with sliding window on univariate data."""
     rng = np.random.default_rng(seed=2)
     series = rng.normal(size=(100,))
     series[50:58] -= 5
+
+    ad_sliding = IDK(psi1=16, psi2=4, width=10, sliding=True, random_state=1)
+    pred_sliding = ad_sliding.fit_predict(series)
+
+    assert pred_sliding.shape == (100,)
+    assert pred_sliding.dtype == np.float64
+    assert 60 <= np.argmax(pred_sliding) <= 80
+
+def test_idk_univariate_custom_series():
+    """Test IDK on a custom univariate series with assert_allclose."""
     series1 = np.array(
         [
             0.18905338,
             -0.52274844,
             -0.41306354,
             -2.44146738,
             1.79970738,
-            1.14416587 - 0.32542284,
+            1.14416587,
+            - 0.32542284,
             0.77380659,
             0.28121067,
             -0.55382284,
         ]
     )
-    y = [0.52333333, 0.19, 0.52333333]
-    ad = IDK(psi1=8, psi2=2, width=1, random_state=2)
-    pred = ad.fit_predict(series)
-    ad_sliding = IDK(psi1=16, psi2=4, width=10, sliding=True, random_state=1)
-    pred_sliding = ad_sliding.fit_predict(series)
-    ad_2 = IDK(psi1=4, psi2=2, width=3, t=10)
+    expected = [0.52333333, 0.19, 0.52333333]
+
+    ad_2 = IDK(psi1=4, psi2=2, width=3, t=10,random_state=2)
     pred2 = ad_2.fit_predict(series1)
-    mae = np.mean(np.abs(y - pred2))
 
-    assert pred.shape == (100,)
-    assert pred.dtype == np.float64
-    assert 50 <= np.argmax(pred) <= 58
-    assert pred_sliding.shape == (91,)
-    assert pred_sliding.dtype == np.float64
-    assert 60 <= np.argmax(pred_sliding) <= 80
-    assert mae < 0.3
+    assert pred2.shape == (3,)
+    assert pred2.dtype == np.float64
+    assert_allclose(pred2, expected, atol=0.01)