| deepanshuk | 297dbd6 | 2020-11-03 13:09:19 +0530 | [diff] [blame] | 1 | # ================================================================================== |
| 2 | # Copyright (c) 2020 HCL Technologies Limited. |
| 3 | # |
| 4 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | # you may not use this file except in compliance with the License. |
| 6 | # You may obtain a copy of the License at |
| 7 | # |
| 8 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | # |
| 10 | # Unless required by applicable law or agreed to in writing, software |
| 11 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | # See the License for the specific language governing permissions and |
| 14 | # limitations under the License. |
| 15 | # ================================================================================== |
| 16 | |
| 17 | Usage of all the programs and files have been mentioned below for the reference. |
| 18 | Need to update this file each time when there is any modifications in the following components. |
| 19 | |
| 20 | main.py: |
| 21 | * Initiates xapp api and runs the entry() using xapp.run() |
| 22 | * If RF model is not present in the path, run train() to train the model for the prediction. |
| 23 | Call predict function for every 1 second(for now as we are using simulated data). |
| 24 | * Read the input csv file that has both normal and anomalous data. |
| 25 | * Simulate diff UEIDs that participate in the anomaly by randomly selecting records from this scoring data set |
| 26 | * Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003) |
| 27 | * Get the acknowledgement message from the traffic steering. |
| 28 | |
| 29 | ad_train.py - Read all the csv files in the current path and create trained model(RF) |
| 30 | |
| 31 | processing.py: |
| 32 | It performs the following activities: |
| 33 | * Columns that are not useful for the prediction will be dropped(UEID, Category, & Timestamp) |
| 34 | * Convert integer and float type into numeric data type. |
| 35 | * verify and drop the highly correlated parameters. |
| 36 | * returns UEID, timestamp and category for the anamolous entries. |
| 37 | |
| 38 | ad_model.py: |
| 39 | * 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). |
| 40 | * Call Predict method to get the final data for the randomly selected UEID. |
| 41 | |
| 42 | tb_format.py: |
| 43 | * start the preprocessing, processing steps using the keycolumns |
| 44 | * populate current timestamp value for MeasTimestampRF |
| 45 | |