call files

src/models/sofia_modelle/call_cnn.py

+from CNN import CNN
+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
+
+#optimal hyperparameters from autors: 'POLY': {'periodicity': 1, 'power': 4}
+params = {
+        'window_size': [50, 100, 150],
+        'num_channel': [[32, 32, 40], [16, 32, 64]]
+    }
+
+def run_CNN(data_train, data_test, window_size=100, num_channel=[32, 32, 40], lr=0.0008, n_jobs=1):
+    clf = CNN(window_size=window_size, num_channel=num_channel, feats=data_test.shape[1], lr=lr, batch_size=128)
+    clf.fit(data_train)
+    score = clf.decision_function(data_test)
+    return score.ravel()
+
+model = 'CNN'
+output_path = '../../../docs/evaluation/'
+
+#writes results in .csv
+main(run_CNN,params,model,data_folders = '../../../data/', model_type='semi-supervised',output_dir = output_path)
+
+#pip3 install -r requirements.txt
+# python src/models/desi/call_poly.py
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src/models/sofia_modelle/call_iforest.py

+from IForest import IForest
+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
+
+#optimal hyperparameters from autors: 'POLY': {'periodicity': 1, 'power': 4}
+params = {
+        'n_estimators': [25, 50, 100, 150, 200]
+    }
+
+def run_IForest(data, slidingWindow=100, n_estimators=100, max_features=1, n_jobs=1):
+    clf = IForest(slidingWindow=slidingWindow, n_estimators=n_estimators, max_features=max_features, n_jobs=n_jobs)
+    clf.fit(data)
+    score = clf.decision_scores_
+    return score.ravel()
+
+model = 'IForest'
+output_path = '../../../docs/evaluation/'
+
+#writes results in .csv
+main(run_IForest,params,model,data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)
+
+#pip3 install -r requirements.txt
+# python src/models/desi/call_poly.py
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src/models/sofia_modelle/call_knn.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
+
+#optimal hyperparameters from autors: 'POLY': {'periodicity': 1, 'power': 4}
+params = {
+        'n_neighbors': [10, 20, 30, 40, 50],
+        'method': ['largest', 'mean', 'median']
+    }
+
+def run_Sub_KNN(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'
+output_path = '../../../docs/evaluation/'
+
+#writes results in .csv
+main(run_Sub_KNN,params,model,data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)
+
+#pip3 install -r requirements.txt
+# python src/models/desi/call_poly.py
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