version 2 with different hyperparameters for knn

src/models/sofia_modelle/call_knn.py

@@ -5,6 +5,7 @@ sys.path.append(str(pathlib.Path.absolute)+ '../../')
 from src.utils.slidingWindows import find_length_rank
 from src.run_model_wrapper import main
 
+#Hyperparameter tested for multivariats
 #optimal hyperparameters from autors: {'n_neighbors': 50, 'method': 'mean'},  
 params = {
         'n_neighbors': [10, 20, 30, 40, 50],

src/models/sofia_modelle/call_knnV2.py

+from KNN import KNN
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path.absolute)+ '../../')
+from src.utils.slidingWindows import find_length_rank
+from src.run_model_wrapper import main
+
+#hyperparameters tested for univariats
+#optimal hyperparameters from autors: {'periodicity': 2, 'n_neighbors': 50},  
+params = {
+        'periodicity': [1, 2, 3],
+        'n_neighbors': [10, 20, 30, 40, 50],
+    }
+
+def run_Sub_KNNV2(data, n_neighbors=10, method='largest', periodicity=1, n_jobs=1):
+    slidingWindow = find_length_rank(data, rank=periodicity)
+    clf = KNN(slidingWindow=slidingWindow, n_neighbors=n_neighbors,method=method, n_jobs=n_jobs)
+    clf.fit(data)
+    score = clf.decision_scores_
+    return score.ravel()
+
+model = 'Sub_KNN_V2'
+output_path = '../../../docs/evaluation/'
+
+#writes results in .csv
+main(run_Sub_KNNV2,params,model,data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)
+
+#pip3 install -r requirements.txt
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