diff --git a/tods/tods_skinterface/primitiveSKI/Base_skinterface.py b/tods/tods_skinterface/primitiveSKI/Base_skinterface.py
index 76ef244..55ab814 100644
--- a/tods/tods_skinterface/primitiveSKI/Base_skinterface.py
+++ b/tods/tods_skinterface/primitiveSKI/Base_skinterface.py
@@ -25,32 +25,33 @@ class BaseSKI():
         self.fit_available = False
         self.predict_available = False
         self.produce_available = False
-        #print(hyperparams)
+
+        # print(hyperparams)
 
     def transform(self, X):     #transform the ndarray to d3m dataframe, select columns to use
-        # if self.use_columns==():
-        #     self.use_columns = [iter for iter in range(len(X))]
-        # else:
-        #     pass
-        # print(self.use_columns)
-
-        use_columns = [iter for iter in range(len(X))]
-        inputs = {}
-        for i in use_columns:
-          inputs['col_'+str(i)] = list(X[i])
-        inputs = container.DataFrame(inputs, columns=list(inputs.keys()), generate_metadata=True)
-        return inputs
+        column_name = [str(col_index) for col_index in range(X.shape[1])]
+        return container.DataFrame(X, columns=column_name, generate_metadata=True)
+
+        # use_columns = [iter for iter in range(len(X))]
+        # inputs = {}
+        # for i in 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):
+        # print(data)
 
         if not self.fit_available:
             raise AttributeError('type object ' + self.__class__.__name__ + ' has no attribute \'fit\'')
 
         data = self.transform(data)
+        # print(data)
         self.set_training_data(data)
+        
         return self.primitive.fit()
     
     def predict(self, data):
@@ -102,4 +103,4 @@ if __name__ == '__main__':
           'use_columns': use_columns,
           'return_result': return_result,
 """
-#use_columns=(-1,), contamination=0.1, return_result='append'
\ No newline at end of file
+#use_columns=(-1,), contamination=0.1, return_result='append'
diff --git a/tods/tods_skinterface/primitiveSKI/detection_algorithm/Telemanom_skinterface.py b/tods/tods_skinterface/primitiveSKI/detection_algorithm/Telemanom_skinterface.py
index 3d9f46f..c6236bc 100644
--- a/tods/tods_skinterface/primitiveSKI/detection_algorithm/Telemanom_skinterface.py
+++ b/tods/tods_skinterface/primitiveSKI/detection_algorithm/Telemanom_skinterface.py
@@ -8,3 +8,4 @@ class TelemanomSKI(BaseSKI):
 		self.fit_available = True
 		self.predict_available = True
 		self.produce_available = False
+
diff --git a/tods/tods_skinterface/test/detection_algorithm/Telemanom_skitest.py b/tods/tods_skinterface/test/detection_algorithm/Telemanom_skitest.py
index 3dc5de7..5942155 100644
--- a/tods/tods_skinterface/test/detection_algorithm/Telemanom_skitest.py
+++ b/tods/tods_skinterface/test/detection_algorithm/Telemanom_skitest.py
@@ -1,10 +1,10 @@
 import numpy as np
 from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI
 
-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]])
+X_train = np.random.rand(9, 3)
+X_test = np.random.rand(9, 3)
 
-transformer = TelemanomSKI()
+transformer = TelemanomSKI(l_s= 2, n_predictions= 1)
 transformer.fit(X_train)
 prediction_labels = transformer.predict(X_test)
 prediction_score = transformer.predict_score(X_test)
@@ -12,3 +12,5 @@ prediction_score = transformer.predict_score(X_test)
 print("Primitive: ", transformer.primitive)
 print("Prediction Labels\n", prediction_labels)
 print("Prediction Score\n", prediction_score)
+
+