fixes

Author: MatthewMiddlehurst

aeon/anomaly_detection/base.py

@@ -17,11 +17,11 @@ class BaseAnomalyDetector(BaseAeonEstimator):
         # todo
     }
 
-    def __init__(self, axis):
+    def __init__(self):
         super().__init__()
 
     @abstractmethod
-    def fit(self, X, y=None, axis=1):
+    def fit(self, X, y=None):
         """Fit time series anomaly detector to X.
 
         If the tag ``fit_is_empty`` is true, this just sets the ``is_fitted`` tag to
@@ -54,7 +54,7 @@ def fit(self, X, y=None, axis=1):
         ...
 
     @abstractmethod
-    def predict(self, X, axis=1) -> np.ndarray:
+    def predict(self, X) -> np.ndarray:
         """Find anomalies in X.
 
         Parameters
@@ -79,7 +79,7 @@ def predict(self, X, axis=1) -> np.ndarray:
         ...
 
     @abstractmethod
-    def fit_predict(self, X, y=None, axis=1) -> np.ndarray:
+    def fit_predict(self, X, y=None) -> np.ndarray:
         """Fit time series anomaly detector and find anomalies for X.
 
         Parameters

aeon/anomaly_detection/collection/base.py

@@ -32,10 +32,11 @@ class name: BaseCollectionAnomalyDetector
 import numpy as np
 import pandas as pd
 
+from aeon.anomaly_detection.base import BaseAnomalyDetector
 from aeon.base import BaseCollectionEstimator
 
 
-class BaseCollectionAnomalyDetector(BaseCollectionEstimator):
+class BaseCollectionAnomalyDetector(BaseCollectionEstimator, BaseAnomalyDetector):
     """
     Abstract base class for collection anomaly detectors.
 
@@ -162,12 +163,65 @@ def predict(self, X):
 
         return self._predict(X)
 
-    @abstractmethod
-    def _fit(self, X, y=None): ...
+    @final
+    def fit_predict(self, X, y=None, axis=1) -> np.ndarray:
+        """Fit time series anomaly detector and find anomalies for X.
+
+        Parameters
+        ----------
+        X : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES
+            The time series to fit the model to.
+            A valid aeon time series data structure. See
+            aeon.base._base_series.VALID_INPUT_TYPES for aeon supported types.
+        y : one of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None
+            The target values for the time series.
+            A valid aeon time series data structure. See
+            aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types.
+        axis : int, default=1
+            The time point axis of the input series if it is 2D. If ``axis==0``, it is
+            assumed each column is a time series and each row is a time point. i.e. the
+            shape of the data is ``(n_timepoints, n_channels)``. ``axis==1`` indicates
+            the time series are in rows, i.e. the shape of the data is
+            ``(n_channels, n_timepoints)``.
+
+        Returns
+        -------
+        np.ndarray
+            A boolean, int or float array of length len(X), where each element indicates
+            whether the corresponding subsequence is anomalous or its anomaly score.
+        """
+        if self.get_tag("requires_y"):
+            if y is None:
+                raise ValueError("Tag requires_y is true, but fit called with y=None")
+
+        # reset estimator at the start of fit
+        self.reset()
+
+        X = self._preprocess_series(X, axis, True)
+
+        if self.get_tag("fit_is_empty"):
+            self.is_fitted = True
+            return self._predict(X)
+
+        if y is not None:
+            y = self._check_y(y)
+
+        pred = self._fit_predict(X, y)
+
+        # this should happen last
+        self.is_fitted = True
+        return pred
+
+    def _fit(self, X, y):
+        return self
 
     @abstractmethod
     def _predict(self, X): ...
 
+    def _fit_predict(self, X, y):
+        self._fit(X, y)
+        return self._predict(X)
+
     def _check_y(self, y, n_cases):
         """Check y input is valid.
 

aeon/anomaly_detection/series/base.py

@@ -9,11 +9,12 @@
 import numpy as np
 import pandas as pd
 
+from aeon.anomaly_detection.base import BaseAnomalyDetector
 from aeon.base import BaseSeriesEstimator
 from aeon.base._base_series import VALID_SERIES_INPUT_TYPES
 
 
-class BaseSeriesAnomalyDetector(BaseSeriesEstimator):
+class BaseSeriesAnomalyDetector(BaseSeriesEstimator, BaseAnomalyDetector):
     """Base class for series anomaly detection algorithms.
 
     Anomaly detection algorithms are used to identify anomalous subsequences in time

aeon/anomaly_detection/series/distance_based/_kmeans.py

@@ -65,7 +65,7 @@ class KMeansAD(BaseSeriesAnomalyDetector):
     Examples
     --------
     >>> import numpy as np
-    >>> from aeon.anomaly_detection.distance_based import KMeansAD
+    >>> from aeon.anomaly_detection.series.distance_based import KMeansAD
     >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64)
     >>> detector = KMeansAD(n_clusters=3, window_size=4, stride=1, random_state=0)
     >>> detector.fit_predict(X)

aeon/anomaly_detection/series/distance_based/_merlin.py

@@ -43,7 +43,7 @@ class MERLIN(BaseSeriesAnomalyDetector):
     Examples
     --------
     >>> import numpy as np
-    >>> from aeon.anomaly_detection.distance_based import MERLIN
+    >>> from aeon.anomaly_detection.series.distance_based import MERLIN
     >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 5, 1, 2, 3, 4])
     >>> detector = MERLIN(min_length=4, max_length=5)
     >>> detector.fit_predict(X)

aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py

@@ -78,7 +78,7 @@ class DWT_MLEAD(BaseSeriesAnomalyDetector):
     Examples
     --------
     >>> import numpy as np
-    >>> from aeon.anomaly_detection.distribution_based import DWT_MLEAD
+    >>> from aeon.anomaly_detection.series.distribution_based import DWT_MLEAD
     >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64)
     >>> detector = DWT_MLEAD(
     ...    start_level=1, quantile_boundary_type='percentile', quantile_epsilon=0.01

aeon/anomaly_detection/series/outlier_detection/_stray.py

@@ -54,7 +54,7 @@ class STRAY(BaseSeriesAnomalyDetector):
 
     Examples
     --------
-    >>> from aeon.anomaly_detection.outlier_detection import STRAY
+    >>> from aeon.anomaly_detection.series.outlier_detection import STRAY
     >>> from aeon.datasets import load_airline
     >>> import numpy as np
     >>> X = load_airline()

aeon/anomaly_detection/series/tests/test_pyod_adapter.py

@@ -6,7 +6,7 @@
 import pytest
 from sklearn.utils import check_random_state
 
-from aeon.anomaly_detection.series.outlier_detection import PyODAdapter
+from aeon.anomaly_detection.series import PyODAdapter
 from aeon.utils.validation._dependencies import _check_soft_dependencies
 
 

aeon/testing/estimator_checking/_yield_estimator_checks.py

@@ -11,8 +11,8 @@
 import numpy as np
 from sklearn.exceptions import NotFittedError
 
+from aeon.anomaly_detection.collection.base import BaseCollectionAnomalyDetector
 from aeon.anomaly_detection.series.base import BaseSeriesAnomalyDetector
-from aeon.anomaly_detection.whole_series.base import BaseCollectionAnomalyDetector
 from aeon.base import BaseAeonEstimator
 from aeon.base._base import _clone_estimator
 from aeon.classification import BaseClassifier

aeon/utils/base/_identifier.py

@@ -47,16 +47,16 @@ def get_identifier(estimator):
     if len(identifiers) == 0:
         raise TypeError("Error, no identifiers could be determined for estimator")
 
-    if len(identifiers) > 1 and "anomaly-detector" in identifiers:
-        identifiers.remove("anomaly-detector")
     if len(identifiers) > 1 and "estimator" in identifiers:
         identifiers.remove("estimator")
-    if len(identifiers) > 1 and "series-estimator" in identifiers:
-        identifiers.remove("series-estimator")
     if len(identifiers) > 1 and "collection-estimator" in identifiers:
         identifiers.remove("collection-estimator")
+    if len(identifiers) > 1 and "series-estimator" in identifiers:
+        identifiers.remove("series-estimator")
     if len(identifiers) > 1 and "transformer" in identifiers:
         identifiers.remove("transformer")
+    if len(identifiers) > 1 and "anomaly-detector" in identifiers:
+        identifiers.remove("anomaly-detector")
 
     if len(identifiers) > 1:
         TypeError(