Create local_outlier_factor.py

projects/piguardian/ai_ml/local_outlier_factor.py

@@ -0,0 +1,23 @@
+import numpy as np
+from sklearn.neighbors import LocalOutlierFactor
+
+class LocalOutlierFactorAnomalyDetector:
+    def __init__(self, n_neighbors=20, algorithm='auto', leaf_size=30, metric='minkowski', p=2):
+        self.n_neighbors = n_neighbors
+        self.algorithm = algorithm
+        self.leaf_size = leaf_size
+        self.metric = metric
+        self.p = p
+        self.lof = LocalOutlierFactor(n_neighbors=n_neighbors, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p)
+
+    def fit(self, X):
+        self.lof.fit(X)
+
+    def predict(self, X):
+        return self.lof.predict(X)
+
+    def decision_function(self, X):
+        return self.lof.decision_function(X)
+
+    def score_samples(self, X):
+        return self.lof.score_samples(X)