[RICAPP-181] Update Prediction message (from QP to TS) with serving cell prediciton

Signed-off-by: Deepanshu Karnwal <deepanshu.k@hcl.com>
Change-Id: Ia14efb7da90a781733b0eab8098ca7e4fc496f08

diff --git a/qp/main.py b/qp/main.py
index 002b063..1c88a48 100644 (file)
--- a/qp/main.py
+++ b/qp/main.py
@@ -74,15 +74,16 @@ def qp_predict_handler(self, summary, sbuf):
         logger.warning("predict handler: failed to send message")
 
 
-def nbcells(ue):
+def cells(ue):
     """
         Extract neighbor cell id for a given UE
     """
     db.read_data(meas='liveUE', limit=1, ueid=ue)
     df = db.data
 
-    nbc = df.filter(regex='nbCell').values[0]
-    return nbc
+    nbc = df.filter(regex='nbCell').values[0].tolist()
+    srvc = df.filter(regex='nrCell').values[0].tolist()
+    return srvc+nbc
 
 
 def predict(payload):
@@ -93,8 +94,8 @@ def predict(payload):
     payload = json.loads(payload)
     ueid = payload['UEPredictionSet'][0]
 
-    nbc = nbcells(ueid)
-    for cid in nbc:
+    cell_list = cells(ueid)
+    for cid in cell_list:
         mcid = cid.replace('/', '')
         db.read_data(meas='liveCell', cellid=cid, limit=11)
         if len(db.data) != 0:

diff --git a/qp/qptrain.py b/qp/qptrain.py
index 68dcb35..2071ac3 100644 (file)
--- a/qp/qptrain.py
+++ b/qp/qptrain.py
@@ -19,6 +19,10 @@ from statsmodels.tsa.stattools import adfuller
 import joblib
 
 
+class DataNotMatchError(Exception):
+    pass
+
+
 class PROCESS(object):
 
     def __init__(self, data):
@@ -61,17 +65,22 @@ class PROCESS(object):
         """ Filter throughput parameters, call make_stationary() to check for Stationarity time series
         """
         df = self.data.copy()
-        df = df[['pdcpBytesDl', 'pdcpBytesUl']]
+        try:
+            df = df[['pdcpBytesDl', 'pdcpBytesUl']]
+        except DataNotMatchError:
+            print('Parameters pdcpBytesDl, pdcpBytesUl does not exist in provided data')
+            self.data = None
         self.data = df.loc[:, (df != 0).any(axis=0)]
         self.make_stationary()  # check for Stationarity and make the Time Series Stationary
 
     def valid(self):
-        df = self.data.copy()
-        df = df.loc[:, (df != 0).any(axis=0)]
-        if len(df) != 0 and df.shape[1] == 2:
-            return True
-        else:
-            return False
+        val = False
+        if self.data is not None:
+            df = self.data.copy()
+            df = df.loc[:, (df != 0).any(axis=0)]
+            if len(df) != 0 and df.shape[1] == 2:
+                val = True
+        return val
 
 
 def train(db, cid):