X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=blobdiff_plain;f=ad%2Fmain.py;fp=ad%2Fmain.py;h=0000000000000000000000000000000000000000;hb=77f7c38a2133e3ca11582a217762802d1a14c8fa;hp=5afb58f17f6b6ddde1f292ab7db05cc709fb424e;hpb=4674f4ac8cbb4f1ca549f6acf32555d9d14c28cb;p=ric-app%2Fad.git

diff --git a/ad/main.py b/ad/main.py
deleted file mode 100644
index 5afb58f..0000000
--- a/ad/main.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# ==================================================================================
-#  Copyright (c) 2020 HCL Technologies Limited.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-# ==================================================================================
-
-import json
-import os
-import pandas as pd
-import schedule
-from ricxappframe.xapp_frame import Xapp
-from ad_model.ad_model import ad_predict, CAUSE
-from ad_train import train
-from ricxappframe.xapp_sdl import SDLWrapper
-from database import DATABASE, DUMMY
-import insert as ins
-
-db = None
-cp = None
-ue_data = None  # needs to be updated in future when live feed will be coming through KPIMON to influxDB
-pos = 0
-sdl = SDLWrapper(use_fake_sdl=True)
-
-
-def entry(self):
-    """  If ML model is not present in the path, It will trigger training module to train the model.
-      Calls predict function every 10 millisecond(for now as we are using simulated data).
-    """
-    if not os.path.isfile('model'):
-        train()
-    schedule.every(0.01).seconds.do(predict, self)
-    while True:
-        schedule.run_pending()
-
-
-def predict(self):
-    """Read the latest ue sample from influxDB and detects if that is anomalous or normal..
-      Send the UEID, DUID, Degradation type and timestamp for the anomalous samples to Traffic Steering (rmr with the message type as 30003)
-      Get the acknowledgement of sent message from the traffic steering.
-    """
-
-    global pos
-    pos = (pos + 1) % len(ue_data)  # iterate through entire list one by one in cycle manner and will be updated when live feed will be coming through KPIMON to influxDB
-    sample = ue_data[pos]
-    ue_df = pd.DataFrame([sample], columns=db.data.columns)
-    val = predict_anomaly(self, ue_df)
-    if (val is not None) and (len(val) > 2):
-        msg_to_ts(self, val)
-
-
-def predict_anomaly(self, df):
-    """ calls ad_predict to detect if given sample is normal or anomalous
-    find out the degradation type if sample is anomalous
-    write given sample along with predicted label to AD measurement
-
-    Parameter
-    ........
-    ue: array or dataframe
-
-    Return
-    ......
-    val: anomalus sample info(UEID, DUID, TimeStamp, Degradation type)
-    """
-    pred = ad_predict(df)
-    df['Anomaly'] = pred
-    df['Degradation'] = ''
-    val = None
-    if 1 in pred:
-        deg = cp.cause(df)
-        if deg:
-            df['Degradation'] = deg
-            db_df = df[['du-id', 'ue-id', 'measTimeStampRf', 'Degradation']]
-
-            # rmr send 30003(TS_ANOMALY_UPDATE), should trigger registered callback
-            result = json.loads(db_df.to_json(orient='records'))
-            val = json.dumps(result).encode()
-            df.loc[db_df.index, 'Degradation'] = db_df['Degradation']
-    df.index = df.measTimeStampRf
-    result = json.loads(df.to_json(orient='records'))
-
-    df = df.drop('measTimeStampRf', axis=1)
-    db.write_anomaly(df, 'AD')
-    return val
-
-
-def msg_to_ts(self, val):
-    # send message from ad to ts
-    print("[INFO] Sending Anomalous UE to TS")
-    success = self.rmr_send(val, 30003)
-    if success:
-        print("[INFO] Message to TS: message sent Successfully")
-    # rmr receive to get the acknowledgement message from the traffic steering.
-    for (summary, sbuf) in self.rmr_get_messages():
-        print("[INFO] Received acknowldgement from TS (TS_ANOMALY_ACK): {}".format(summary))
-        self.rmr_free(sbuf)
-
-
-def connectdb(thread=False):
-    # Create a connection to InfluxDB if thread=True, otherwise it will create a dummy data instance
-    global db
-    global cp
-    global ue_data
-    if thread:
-        db = DUMMY()
-    else:
-        ins.populatedb()  # temporary method to populate db, it will be removed when data will be coming through KPIMON to influxDB
-
-        db = DATABASE('UEData')
-        db.read_data("liveUE")
-        ue_data = db.data.values.tolist()  # needs to be updated in future when live feed will be coming through KPIMON to influxDB
-    cp = CAUSE(db)
-
-
-def start(thread=False):
-    # Initiates xapp api and runs the entry() using xapp.run()
-    xapp = Xapp(entrypoint=entry, rmr_port=4560, use_fake_sdl=False)
-    connectdb(thread)
-    xapp.run()