| # ================================================================================== |
| # 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. |
| # ================================================================================== |
| |
| Usage of all the programs and files have been mentioned below for the reference. |
| Need to update this file each time when there is any modifications in the following components. |
| |
| main.py: |
| * Initiates xapp api and runs the entry() using xapp.run() |
| * If RF model is not present in the path, run train() to train the model for the prediction. |
| Call predict function for every 1 second(for now as we are using simulated data). |
| * Read the input csv file that has both normal and anomalous data. |
| * Simulate diff UEIDs that participate in the anomaly by randomly selecting records from this scoring data set |
| * Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003) |
| * Get the acknowledgement message from the traffic steering. |
| |
| ad_train.py - Read all the csv files in the current path and create trained model(RF) |
| |
| processing.py: |
| It performs the following activities: |
| * Columns that are not useful for the prediction will be dropped(UEID, Category, & Timestamp) |
| * Convert integer and float type into numeric data type. |
| * verify and drop the highly correlated parameters. |
| * returns UEID, timestamp and category for the anamolous entries. |
| |
| ad_model.py: |
| * Extract all the unique UEID and filters only the randomly selected UEID(this step will be removed when we implement in sdl way of getting the UEID). |
| * Call Predict method to get the final data for the randomly selected UEID. |
| |
| tb_format.py: |
| * start the preprocessing, processing steps using the keycolumns |
| * populate current timestamp value for MeasTimestampRF |
| |