[feat] add AENN use case

Author: TVect

NoiseFiltersPy/AENN.py

@@ -1,3 +1,7 @@
+"""
+@ref: [An Experiment with the Edited Nearest-Neighbor Rule](https://ieeexplore.ieee.org/ielx5/21/4309513/04309523.pdf)
+"""
+
 from sklearn.model_selection import KFold
 from sklearn.neighbors import KNeighborsClassifier
 import numpy as np
@@ -9,18 +13,30 @@
 class AENN:
     def __init__(self, max_neighbours: int = 5, n_jobs: int = -1):
         self.max_neighbours = max_neighbours
-        self.filter = Filter(parameters = {"max_neighbours": self.max_neighbours})
+        self.filter = Filter(parameters={"max_neighbours": self.max_neighbours})
         self.n_jobs = n_jobs
 
     def __call__(self, data: t.Sequence, classes: t.Sequence) -> Filter:
         self.isNoise = np.array([False] * len(classes))
         for n_neigh in range(1, self.max_neighbours + 1):
-            self.clf = KNeighborsClassifier(n_neighbors = n_neigh, algorithm = 'kd_tree', n_jobs = self.n_jobs)
+            self.clf = KNeighborsClassifier(n_neighbors=n_neigh, algorithm='kd_tree', n_jobs=self.n_jobs)
             for indx in np.argwhere(np.invert(self.isNoise)):
-                self.clf.fit(np.delete(data, indx, axis = 0), np.delete(classes, indx, axis = 0))
+                self.clf.fit(np.delete(data, indx, axis=0), np.delete(classes, indx, axis=0))
                 pred = self.clf.predict(data[indx])
                 self.isNoise[indx] = pred != classes[indx]
+            print(f"n_neigh: {n_neigh}, is_noise count:, {sum(self.isNoise)}, total: {len(self.isNoise)}")
         self.filter.rem_indx = np.argwhere(self.isNoise)
         notNoise = np.invert(self.isNoise)
         self.filter.set_cleanData(data[notNoise], classes[notNoise])
         return self.filter
+
+
+if __name__ == "__main__":
+    from sklearn.datasets import load_iris
+
+    data = load_iris()
+    rm_feature_id = data.feature_names.index("petal length (cm)")
+    features = data.data[:, [idx for idx in range(len(data.feature_names)) if idx != rm_feature_id]]
+    labels = data.target
+    filter = AENN()(features, labels)
+    print(filter.rem_indx.shape, filter.rem_indx[:, 0])