[ENH] Implementation of LOF using PyODAdapter (#2209)

* Implementation of LOF using PyODAdapter

* Automatic `pre-commit` fixes

* window size > stride in params

* NotFittederror

* Automatic `pre-commit` fixes

* return self

* Automatic `pre-commit` fixes

* test to check params

* Adding a value to window_size if not provided

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Changes in test, 1 failing

* Automatic `pre-commit` fixes

* pyod_model as parameter

* unfinished changes

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* working model

* Automatic `pre-commit` fixes

* merge conflicts

* .

* Automatic `pre-commit` fixes

* git conflicts

* Automatic `pre-commit` fixes

* git conflicts

* test

* Automatic `pre-commit` fixes

* test2

* test3

* Automatic `pre-commit` fixes

* .

* fixes

* fixed minor mistakes

* Update aeon/anomaly_detection/tests/test_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/tests/test_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/tests/test_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Update aeon/anomaly_detection/_lof.py

Co-authored-by: Sebastian Schmidl <[email protected]>

* Changes in test_lof

* removed anomaly range stuff

* removed unnecessary in test_lof_default

* checking for anomalies

---------

Co-authored-by: aryanpola <[email protected]>
Co-authored-by: Sebastian Schmidl <[email protected]>
Co-authored-by: Sebastian Schmidl <[email protected]>

aeon/anomaly_detection/__init__.py

@@ -1,17 +1,18 @@
 """Time Series Anomaly Detection."""
 
 __all__ = [
+    "CBLOF",
+    "COPOD",
     "DWT_MLEAD",
+    "IsolationForest",
     "KMeansAD",
+    "LeftSTAMPi",
+    "LOF",
     "MERLIN",
-    "STRAY",
+    "OneClassSVM",
     "PyODAdapter",
     "STOMP",
-    "LeftSTAMPi",
-    "IsolationForest",
-    "CBLOF",
-    "COPOD",
-    "OneClassSVM",
+    "STRAY",
 ]
 
 from aeon.anomaly_detection._cblof import CBLOF
@@ -20,6 +21,7 @@
 from aeon.anomaly_detection._iforest import IsolationForest
 from aeon.anomaly_detection._kmeans import KMeansAD
 from aeon.anomaly_detection._left_stampi import LeftSTAMPi
+from aeon.anomaly_detection._lof import LOF
 from aeon.anomaly_detection._merlin import MERLIN
 from aeon.anomaly_detection._one_class_svm import OneClassSVM
 from aeon.anomaly_detection._pyodadapter import PyODAdapter

aeon/anomaly_detection/_lof.py

@@ -0,0 +1,169 @@
+"""Local Outlier Factor (LOF) algorithm for anomaly detection."""
+
+__maintainer__ = []
+__all__ = ["LOF"]
+
+from typing import Optional, Union
+
+import numpy as np
+
+from aeon.anomaly_detection._pyodadapter import PyODAdapter
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+class LOF(PyODAdapter):
+    """Local Outlier Factor (LOF) algorithm for anomaly detection.
+
+    This class implement metrics-based outlier detection algorithms using the
+    Local Outlier Factor (LOF) algorithm from PyOD.
+
+    .. list-table:: Capabilities
+       :stub-columns: 1
+
+       * - Input data format
+         - univariate or multivariate
+       * - Output data format
+         - anomaly scores
+       * - missing_values
+         - False
+       * - Learning Type
+         - unsupervised or semi-supervised
+       * - python_dependencies
+         - ["pyod"]
+
+    The documentation for parameters has been adapted from the
+    [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id586).
+    Here, `X` refers to the set of sliding windows extracted from the time series
+    using :func:`aeon.utils.windowing.sliding_windows` with the parameters
+    ``window_size`` and ``stride``. The internal `X` has the shape
+    `(n_windows, window_size * n_channels)`.
+
+    Parameters
+    ----------
+    n_neighbors : int, optional (default=20)
+        Number of neighbors to use by default for `kneighbors` queries.
+        If n_neighbors is larger than the number of samples provided,
+        all samples will be used.
+    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
+        Algorithm used to compute the nearest neighbors:
+        - 'ball_tree' will use BallTree
+        - 'kd_tree' will use KDTree
+        - 'brute' will use a brute-force search.
+        - 'auto' will attempt to decide the most appropriate algorithm
+          based on the values passed to :meth:`fit` method.
+        Note: fitting on sparse input will override the setting of
+        this parameter, using brute force.
+    leaf_size : int, optional (default=30)
+        Leaf size passed to `BallTree` or `KDTree`. This can
+        affect the speed of the construction and query, as well as the memory
+        required to store the tree. The optimal value depends on the
+        nature of the problem.
+    metric : string or callable, default 'minkowski'
+        metric used for the distance computation. Any metric from scikit-learn
+        or scipy.spatial.distance can be used.
+        If metric is a callable function, it is called on each
+        pair of instances (rows) and the resulting value recorded. The callable
+        should take two arrays as input and return one value indicating the
+        distance between them. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
+    p : integer, optional (default = 2)
+        Parameter for the Minkowski metric
+    metric_params : dict, optional (default = None)
+        Additional keyword arguments for the metric function.
+    n_jobs : int, optional (default = 1)
+        The number of parallel jobs to run for neighbors search.
+        If ``-1``, then the number of jobs is set to the number of CPU cores.
+        Affects only kneighbors and kneighbors_graph methods.
+    novelty : bool (default=False)
+        By default, LocalOutlierFactor is only meant to be used for outlier
+        detection (novelty=False). Set novelty to True if you want to use
+        LocalOutlierFactor for novelty detection. In this case be aware that
+        that you should only use predict, decision_function and score_samples
+        on new unseen data and not on the training set.
+    """
+
+    _tags = {
+        "capability:multivariate": True,
+        "capability:univariate": True,
+        "capability:missing_values": False,
+        "fit_is_empty": False,
+        "python_dependencies": ["pyod"],
+    }
+
+    def __init__(
+        self,
+        n_neighbors: int = 20,
+        algorithm: Optional[str] = "auto",
+        leaf_size: int = 30,
+        metric: str = "minkowski",
+        p: int = 2,
+        metric_params: Optional[dict] = None,
+        n_jobs: int = 1,
+        window_size: int = 10,
+        stride: int = 1,
+    ):
+        _check_soft_dependencies(*self._tags["python_dependencies"])
+        from pyod.models.lof import LOF as PyOD_LOF
+
+        # Set a default contamination value internally
+        contamination = 0.1
+
+        model = PyOD_LOF(
+            n_neighbors=n_neighbors,
+            algorithm=algorithm,
+            leaf_size=leaf_size,
+            metric=metric,
+            p=p,
+            metric_params=metric_params,
+            n_jobs=n_jobs,
+            contamination=contamination,  # Only for PyOD LOF
+            novelty=False,  # Initialize unsupervised LOF (novelty=False)
+        )
+        self.n_neighbors = n_neighbors
+        self.algorithm = algorithm
+        self.leaf_size = leaf_size
+        self.metric = metric
+        self.p = p
+        self.metric_params = metric_params
+        self.n_jobs = n_jobs
+        super().__init__(pyod_model=model, window_size=window_size, stride=stride)
+
+    def _fit(self, X: np.ndarray, y: Union[np.ndarray, None] = None) -> None:
+        # Set novelty to True for semi-supervised learning
+        self.pyod_model.novelty = True
+        super()._fit(X, y)
+
+    def _predict(self, X: np.ndarray) -> np.ndarray:
+        return super()._predict(X)
+
+    def _fit_predict(
+        self, X: np.ndarray, y: Union[np.ndarray, None] = None
+    ) -> np.ndarray:
+        # Set novelty to False for unsupervised learning
+        self.pyod_model.novelty = False
+        return super()._fit_predict(X, y)
+
+    @classmethod
+    def _get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests.
+
+        Returns
+        -------
+        params : dict
+            Parameters to create testing instances of the class.
+            Each dict corresponds to parameters that will create an "interesting"
+            test instance.
+        """
+        # Define a test parameter set with different combinations of parameters
+        return {
+            "n_neighbors": 5,
+            "leaf_size": 10,
+            "p": 2,
+            "window_size": 10,
+            "stride": 2,
+        }