[ENH, REF]Refactored time-point based ROCKAD implementation

.github/workflows/periodic_tests.yml

@@ -178,7 +178,7 @@ jobs:
         if: runner.os == 'Linux'
         uses: pierotofy/[email protected]
         with:
-          swap-size-gb: 8
+          swap-size-gb: 4
 
       - name: Use numba cache to set env variables but not restore cache
         uses: ./.github/actions/numba_cache

.github/workflows/pr_pytest.yml

@@ -76,7 +76,7 @@ jobs:
         if: runner.os == 'Linux'
         uses: pierotofy/[email protected]
         with:
-          swap-size-gb: 8
+          swap-size-gb: 4
 
       - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }}
         name: Restore numba cache

.github/workflows/release.yml

@@ -69,7 +69,7 @@ jobs:
         if: runner.os == 'Linux'
         uses: pierotofy/[email protected]
         with:
-          swap-size-gb: 8
+          swap-size-gb: 4
 
       - uses: actions/download-artifact@v4
         with:

aeon/anomaly_detection/series/distance_based/_rockad.py

@@ -1,5 +1,6 @@
 """ROCKAD anomaly detector."""
 
+__maintainer__ = []
 __all__ = ["ROCKAD"]
 
 import warnings
@@ -17,14 +18,16 @@
 
 class ROCKAD(BaseSeriesAnomalyDetector):
     """
-    ROCKET-based Anomaly Detector (ROCKAD).
+    ROCKET-based Semi-Supervised Anomaly Detector (ROCKAD).
 
+    Adapted ROCKAD [1]_ version to detect anomalies on time-points.
     ROCKAD leverages the ROCKET transformation for feature extraction from
     time series data and applies the scikit learn k-nearest neighbors (k-NN)
     approach with bootstrap aggregation for robust anomaly detection.
     After windowing, the data gets transformed into the ROCKET feature space.
     Then the windows are compared based on the feature space by
-    finding the nearest neighbours.
+    finding the nearest neighbours. Whole-series based ROCKAD as proposed in
+    [1]_ can be found at aeon/anomaly_detection/collection/_rockad.py
 
     This class supports both univariate and multivariate time series and
     provides options for normalizing features, applying power transformations,
@@ -61,6 +64,31 @@ class ROCKAD(BaseSeriesAnomalyDetector):
         List containing k-NN estimators used for anomaly scoring, set after fitting.
     power_transformer_ : PowerTransformer
         Transformer used to apply power transformation to the features.
+
+    References
+    ----------
+    .. [1] Theissler, A., Wengert, M., Gerschner, F. (2023).
+        ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection.
+        In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent
+        Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science,
+        vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_33
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from aeon.anomaly_detection.series.distance_based import ROCKAD
+    >>> rng = np.random.default_rng(seed=42)
+    >>> X_train = rng.normal(loc=0.0, scale=1.0, size=(1000,))
+    >>> X_test = rng.normal(loc=0.0, scale=1.0, size=(20,))
+    >>> X_test[15:20] -= 5
+    >>> detector = ROCKAD(window_size=15,n_estimators=10,n_kernels=10,n_neighbors=3)
+    >>> detector.fit(X_train)
+    ROCKAD(...)
+    >>> detector.predict(X_test)
+    array([0.        , 0.00554713, 0.0699094 , 0.22881059, 0.32382585,
+           0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
+           0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
+           0.52382585, 0.65200875, 0.80313368, 0.85194345, 1.        ])
     """
 
     _tags = {
@@ -86,7 +114,6 @@ def __init__(
         n_jobs=1,
         random_state=42,
     ):
-
         self.n_estimators = n_estimators
         self.n_kernels = n_kernels
         self.normalise = normalise
@@ -136,7 +163,6 @@ def _check_params(self, X: np.ndarray) -> None:
             )
 
     def _inner_fit(self, X: np.ndarray) -> None:
-
         self.rocket_transformer_ = Rocket(
             n_kernels=self.n_kernels,
             normalise=self.normalise,
@@ -189,7 +215,6 @@ def _inner_fit(self, X: np.ndarray) -> None:
             self.list_baggers_.append(estimator)
 
     def _predict(self, X) -> np.ndarray:
-
         _X, padding = sliding_windows(
             X, window_size=self.window_size, stride=self.stride, axis=0
         )
@@ -209,22 +234,8 @@ def _fit_predict(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> np.ndar
         return point_anomaly_scores
 
     def _inner_predict(self, X: np.ndarray, padding: int) -> np.ndarray:
-
-        anomaly_scores = self._predict_proba(X)
-
-        point_anomaly_scores = reverse_windowing(
-            anomaly_scores, self.window_size, np.nanmean, self.stride, padding
-        )
-
-        point_anomaly_scores = (point_anomaly_scores - point_anomaly_scores.min()) / (
-            point_anomaly_scores.max() - point_anomaly_scores.min()
-        )
-
-        return point_anomaly_scores
-
-    def _predict_proba(self, X):
         """
-        Predicts the probability of anomalies for the input data.
+        Predict the anomaly score for each time-point in the input data.
 
         Parameters
         ----------
@@ -259,6 +270,14 @@ def _predict_proba(self, X):
             y_scores[:, idx] = scores
 
         # Average the scores to get the final score for each time series
-        y_scores = y_scores.mean(axis=1)
+        anomaly_scores = y_scores.mean(axis=1)
 
-        return y_scores
+        point_anomaly_scores = reverse_windowing(
+            anomaly_scores, self.window_size, np.nanmean, self.stride, padding
+        )
+
+        point_anomaly_scores = (point_anomaly_scores - point_anomaly_scores.min()) / (
+            point_anomaly_scores.max() - point_anomaly_scores.min()
+        )
+
+        return point_anomaly_scores

aeon/anomaly_detection/series/distance_based/tests/test_rockad.py

@@ -10,8 +10,9 @@
 def test_rockad_univariate():
     """Test ROCKAD univariate output."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100,))
-    series[50:58] -= 5
+    train_series = rng.normal(size=(100,))
+    test_series = rng.normal(size=(100,))
+    test_series[50:58] -= 5
 
     ad = ROCKAD(
         n_estimators=100,
@@ -22,7 +23,8 @@ def test_rockad_univariate():
         stride=1,
     )
 
-    pred = ad.fit_predict(series, axis=0)
+    ad.fit(train_series, axis=0)
+    pred = ad.predict(test_series, axis=0)
 
     assert pred.shape == (100,)
     assert pred.dtype == np.float64
@@ -32,9 +34,10 @@ def test_rockad_univariate():
 def test_rockad_multivariate():
     """Test ROCKAD multivariate output."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100, 3))
-    series[50:58, 0] -= 5
-    series[87:90, 1] += 0.1
+    train_series = rng.normal(size=(100, 3))
+    test_series = rng.normal(size=(100, 3))
+    test_series[50:58, 0] -= 5
+    test_series[87:90, 1] += 0.1
 
     ad = ROCKAD(
         n_estimators=1000,
@@ -45,7 +48,8 @@ def test_rockad_multivariate():
         stride=1,
     )
 
-    pred = ad.fit_predict(series, axis=0)
+    ad.fit(train_series, axis=0)
+    pred = ad.predict(test_series, axis=0)
 
     assert pred.shape == (100,)
     assert pred.dtype == np.float64
@@ -55,21 +59,28 @@ def test_rockad_multivariate():
 def test_rockad_incorrect_input():
     """Test ROCKAD incorrect input."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100,))
+    train_series = rng.normal(size=(100,))
+    test_series = rng.normal(size=(5,))
 
     with pytest.raises(ValueError, match="The window size must be at least 1"):
         ad = ROCKAD(window_size=0)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.raises(ValueError, match="The stride must be at least 1"):
         ad = ROCKAD(stride=0)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.raises(
         ValueError, match=r"Window count .* has to be larger than n_neighbors .*"
     ):
         ad = ROCKAD(stride=1, window_size=100)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.warns(
         UserWarning, match=r"Power Transform failed and thus has been disabled."
     ):
         ad = ROCKAD(stride=1, window_size=5)
-        ad.fit_predict(series)
+        ad.fit(train_series)
+    with pytest.raises(
+        ValueError, match=r"window shape cannot be larger than input array shape"
+    ):
+        ad = ROCKAD(stride=1, window_size=10)
+        ad.fit(train_series)
+        ad.predict(test_series)