X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=blobdiff_plain;f=ad%2Fad_model%2Fad_model.py;h=cf517b0b789f99b475ac034bf92f780c6314f3a1;hb=588acf17f5f14399b2bad62bda8106fef7dac063;hp=eee9b677dc056d1fb74e381448269cc16d0a0869;hpb=4f8b2a6fc8581b8227489d857f06d7734883700a;p=ric-app%2Fad.git

diff --git a/ad/ad_model/ad_model.py b/ad/ad_model/ad_model.py
index eee9b67..cf517b0 100644
--- a/ad/ad_model/ad_model.py
+++ b/ad/ad_model/ad_model.py
@@ -14,60 +14,59 @@
 #  limitations under the License.
 # ==================================================================================
 
-import hdbscan
-import pandas as pd
-import numpy as np
 import joblib
 import random
 import json
-    
+
+
 class modelling(object):
-    def __init__(self,data):
+    def __init__(self, data):
         """ Separating UEID and timestamp features to be mapped later after prediction  """
         self.time = data.MeasTimestampRF
         self.id = data.UEID
-        self.data = data.drop(['UEID', 'MeasTimestampRF'], axis = 1)
-        
+        self.data = data.drop(['UEID', 'MeasTimestampRF'], axis=1)
+
     def predict(self, name):
-        """  
+        """
            Load the saved model and map the predicted category into Category field.
-           Map UEID, MeasTimestampRF with the predicted result. 
+           Map UEID, MeasTimestampRF with the predicted result.
         """
-        model = joblib.load('/tmp/ad/' + name)
+        model = joblib.load('ad/' + name)
         pred = model.predict(self.data)
         data = self.data.copy()
-        le = joblib.load('/tmp/ad/LabelEncoder')
+        le = joblib.load('ad/LabelEncoder')
         data['Category'] = le.inverse_transform(pred)
         data['MeasTimestampRF'] = self.time
         data['UEID'] = self.id
         return data
 
+
 def compare(df):
     """
      If the category of UEID is present in the segment file, it is considered as normal(0)
      otherwise, the sample is considered as anomaly.
-    """    
-    with open("/tmp/ad/ue_seg.json", "r") as json_data:
+    """
+    with open("ad/ue_seg.json", "r") as json_data:
         segment = json.loads(json_data.read())
     anomaly = []
     for i in df.index:
-        if df.loc[i, 'Category'] in segment[str(df.loc[i,'UEID'])]:
+        if df.loc[i, 'Category'] in segment[str(df.loc[i, 'UEID'])]:
             anomaly.append(0)
         else:
             anomaly.append(1)
     return anomaly
-        
+
+
 def HDB_PREDICT(df):
     """
-        Extract all the unique UEID 
+        Extract all the unique UEID
         Call Predict method to get the final data for the randomly selected UEID
     """
     ue_list = df.UEID.unique()  # Extract unique UEIDs
-    ue = random.choice(ue_list) # Randomly selected the ue list
+    ue = random.choice(ue_list)  # Randomly selected the ue list
     df = df[df['UEID'] == ue]
     db = modelling(df)
-    db_df = db.predict('RF')# Calls predict module and store the result into db_df
+    db_df = db.predict('RF')  # Calls predict module and store the result into db_df
     del db
-    
     db_df['Anomaly'] = compare(db_df)
     return db_df