remove extras

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tods/utils/interface.py

@@ -1,71 +0,0 @@
-from d3m import container
-from tods.detection_algorithm import DeepLog
-from tods.detection_algorithm.PyodABOD import ABODPrimitive
-from tods.detection_algorithm.PyodAE import AutoEncoderPrimitive
-from tods.detection_algorithm.PyodSOD import SODPrimitive
-from tods.detection_algorithm.AutoRegODetect import AutoRegODetectorPrimitive
-
-class SKInterface():
-    def __init__(self, primitive, hyperparameter=None):
-        hyperparams_class = primitive.metadata.get_hyperparams()
-        hyperparams = hyperparams_class.defaults()
-        if hyperparameter is not None:
-            hyperparams = hyperparams.replace(hyperparameter)
-            
-        self.primitive = primitive(hyperparams=hyperparams)
-        self.use_columns = hyperparams['use_columns']
-        print(hyperparams)
-
-    def transform(self, X):
-        if self.use_columns==():
-            self.use_columns = [iter for iter in range(len(X))]         
-        else:
-            pass
-
-        inputs = {}
-        for i in self.use_columns:
-          inputs['col_'+str(i)] = list(X[i])
-        inputs = container.DataFrame(inputs, columns=list(inputs.keys()), generate_metadata=True)
-        return inputs
-
-    def set_training_data(self, data):
-        return self.primitive.set_training_data(inputs=data)
-
-    def fit(self, data):
-        data = self.transform(data)
-        self.set_training_data(data)
-        return self.primitive.fit()
-    
-    def produce(self, data):
-        data = self.transform(data)
-        return self.primitive.produce(inputs=data).value
-    
-    def produce_score(self, data):
-        data = self.transform(data)
-        return self.primitive.produce_score(inputs=data).value
-
-if __name__ == '__main__':
-    import numpy as np
-    X_train = np.array([[3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]])
-    X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
-    transformer = SKInterface(AutoRegODetectorPrimitive)
-    transformer.fit(X_train)
-    prediction_labels = transformer.produce(X_test)
-    prediction_score = transformer.produce_score(X_test)
-    print("Prediction Labels\n", prediction_labels)
-    print("Prediction Score\n", prediction_score)
-
-"""
-  def transform(self, X):
-    inputs = {}
-    for i in range(len(X)):
-      inputs['col_'+str(i)] = list(X[i])
-    inputs = container.DataFrame(inputs, columns=list(inputs.keys()), generate_metadata=True)
-    outputs = self.primitive.produce(inputs=inputs).value.to_numpy()
-    return outputs
-
-    'contamination': contamination,
-          'use_columns': use_columns,
-          'return_result': return_result,
-"""
-#use_columns=(-1,), contamination=0.1, return_result='append'

tods/utils/test_main.py

@@ -1,14 +0,0 @@
-import numpy as np
-from skinterface.primitiveSKI.AutoRegODetector_skinterface import AutoRegODetectorSKI
-
-X_train = np.array([[3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]])
-X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
-
-transformer = AutoRegODetectorSKI(contamination=0.2, window_size=2)
-transformer.fit(X_train)
-prediction_labels = transformer.predict(X_test)
-prediction_score = transformer.predict_score(X_test)
-
-print("Primitive: ", transformer.primitive)
-print("Prediction Labels\n", prediction_labels)
-print("Prediction Score\n", prediction_score)