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# ============LICENSE_START=======================================================
# ml-prediction-ms
# ================================================================================
# Copyright (C) 2023 Wipro 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.
# ============LICENSE_END=========================================================
import pytest
from src.run import Parser, Prediction, Controller
import unittest
import requests
import responses
from unittest import TestCase
from unittest import mock
from mock import patch # for Python >= 3.3 use unittest.mock
import pandas as pd
import numpy as np
from pandas import DataFrame, read_csv, read_excel
from pandas import concat
from tensorflow.keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
from numpy import concatenate
from sklearn.metrics import mean_squared_error
from math import sqrt
from datetime import datetime
import json
import time
import requests
from requests.auth import HTTPBasicAuth
from confluent_kafka import Consumer
from confluent_kafka import Producer
import socket
import requests_mock
from mock import patch
# This method will be used by the mock to replace requests.get
def mocked_requests_get(*args, **kwargs):
class MockResponse:
def __init__(self, json_data, status_code):
self.json_data = json_data
self.status_code = status_code
def json(self):
return self.json_data
return MockResponse({"key1": "value1"}, 200)
# Our test case class
class ControllerTestCase(unittest.TestCase):
# We patch 'requests.get' with our own method. The mock object is passed in to our test case method.
@mock.patch('requests.get', side_effect=mocked_requests_get)
def test_GetData(self, mock_get):
status = True
# Assert requests.get calls
ctl = Controller()
conf = {'bootstrap.servers': "kafka:9092",'group.id': "1",'auto.offset.reset': 'smallest'}
consumer = Consumer(conf)
consumer.subscribe(([ctl.Config_Object.get_DataTopic(), 1]))
# We can even assert that our mocked method was called with the right parameters
msg = consumer.poll(1)
#json_data = msg.value().decode('utf-8')
#assert len(msg) != 0, "the list is non empty"
assert status != False
def test_simulatedTestDataToReplaceTopic(self):
self.Controller_Object = Controller()
status = self.Controller_Object.simulatedTestDataToReplaceTopic()
assert status != False
def test_PreprocessAndPredict(self):
ctl = Controller()
# Opening JSON file
f = open('tests/unit/sample.json',)
# returns JSON object as
# a dictionary
json_data = json.load(f)
status = ctl.PreprocessAndPredict(json_data)
assert status != False
# This method will be used by the mock to replace requests.POST
def mocked_requests_post(*args, **kwargs):
class MockResponse:
def __init__(self, json_data, status_code):
self.json_data = json_data
self.status_code = status_code
def json(self):
return self.json_data
return MockResponse({"key1": "value1"}, 200)
#return MockResponse(None, 404)
# Our test case class
class PredictionTestCase(unittest.TestCase):
# We patch 'requests.get' with our own method. The mock object is passed in to our test case method.
@mock.patch('requests.post', side_effect=mocked_requests_post)
def test_IsPolicyUpdate_url_Exist(self, mock_post):
# Assert requests.post calls
pred = Prediction()
status = pred.IsPolicyUpdate_url_Exist()
assert status == True, "Failed"
class TestPredict(unittest.TestCase):
def test_Parser(self):
Controller_Object = Controller()
conf = {'bootstrap.servers': "kafka:9092",'group.id': "1",'auto.offset.reset': 'smallest'}
consumer = Consumer(conf)
consumer.subscribe(([self.Config_Object.get_DataTopic(), -1]))
pm_data = Controller_Object.GetData(consumer)
Parser_Object = Parser()
data_dic={}
status = False
len_pm_data=len(pm_data)
for i in range(len_pm_data):
temp_data=json.loads(pm_data[i])
sub_data = temp_data['event']['perf3gppFields']['measDataCollection']['measInfoList'][0]
server_name = temp_data['event']['perf3gppFields']['measDataCollection']['measuredEntityDn']
features=sub_data['measTypes']['sMeasTypesList']
features.extend(['_maxNumberOfConns.configured', '_maxNumberOfConns.predicted'])
slice_name=features[0].split('.')[2]
data_val= sub_data['measValuesList']
data_dic= Parser_Object.Data_Parser(data_val,data_dic,features,slice_name)
data_df=pd.DataFrame(data_dic)
if len(data_df)<window_size+1:
continue
else:
status = True
assert status == False, "Failed"
def test_Parser(self):
data_dic={}
Parser_Object=Parser()
data_val={}
features={}
slice_name=""
data_dic= Parser_Object.Data_Parser(data_val,data_dic,features,slice_name)
assert data_dic == {}, "Failed"
def test_Post_Config_Topic(self):
window_size=4
self.Predict_Object=Prediction()
df = pd.read_excel('tests/unit/test.xlsx', engine='openpyxl')
new_columns1=[]
len_dfcolumns=len(df.columns)
for i in range(len_dfcolumns):
new_columns1.append('01-B989BD_'+df.columns[i])
df.columns=new_columns1
slice_name=df.columns[0].split('.')[0]
data_df=pd.DataFrame()
len_df=len(df)
for i in range(len_df-1):
temp_df=df.iloc[[i]]
data_df=data_df.append(temp_df)
# parse pm data + configured data + predicted dummy data(=configured data- to be changed after pred)
if len(data_df)<window_size+1:
continue
configured={}
predicted={}
len_data_dfcol=len(data_df.columns)
for x in range(0,len_data_dfcol,window_size+1):
test=data_df.iloc[-5:,x:x+5]
cell=test.columns[0].split('_')[1]
inv_yhat = self.Predict_Object.Predict_Model(test) # Predict using model
configured[cell]= test.iat[-2,4]
inv_yhat = float(inv_yhat[:,-1])
predicted[cell]=inv_yhat
updated_predicted= self.Predict_Object.Logic(list(configured.values()), list(predicted.values()))
count=0
for x in range(0,len_data_dfcol, window_size+1):
data_df.iloc[[i],[x+4]]= updated_predicted[count]
count+=1
status = self.Predict_Object.Final_Post_Method(predicted, configured, slice_name, 'cucpserver1') #hardcoding the server name
if status == False:
break
assert status == True, "Failed"
'''def test_Execute(self):
self.Controller_Object = Controller()
status = self.Controller_Object.Execute()
assert bool(status) != False'''
if __name__ == '__main__':
unittest.main()
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