Merge branch 'feature/role-stage-integration' into develop

bin/deploy.py

@@ -17,7 +17,7 @@ def print_help():
 def get_microservice_name_from_path(path) -> str:
     '''
     Extracts the microservice name from the path.
-    :param path: The path, eg. src\data-hub\stage-discovery-microservice\deployment
+    :param path: The path, eg. src\\data-hub\\stage-discovery-microservice\\deployment
     '''
     name = path.split(os.path.normpath('/'))[-2]
     
@@ -75,7 +75,7 @@ if __name__ == '__main__':
     img_name = None
     if len(sys.argv) > 2:
         img_name = sys.argv[2]
-        deployment_file_paths = [p for p in deployment_file_paths if (img_name in p)]
+        deployment_file_paths = [p for p in deployment_file_paths if (img_name == get_microservice_name_from_path(p))]
 
     error_val = 0
     if command == 'delete' or command == 'redeploy':

src/data-hub/agent-discovery-microservice/README.md

-# Agent Discovery Microservice
-The semantic linking microservice labels anonymous logins with individual agents, e.g. a human or an automated device.
-
-## Technologies
-- Python 3.x
-- Python module Flask
-- Python module Connexion with Swagger
-- Docker
-- Kubernetes
\ No newline at end of file

src/data-hub/agent-discovery-microservice/app/configs/swagger.yml

-swagger: "2.0"
-info:
-  title: Agent Discovery microservice
-  description: This is the documentation for the agent discovery microservice.
-  version: "1.0.0"
-
-consumes:
-  - "application/json"
-produces:
-  - "application/json"
-
-basePath: "/api"
-
-paths:
-  /debug:
-    post:
-      operationId: "debug.echo"
-      tags:
-        - "Echo"
-      summary: "Echo function for debugging purposes"
-      description: "Echoes the input back to the caller."
-      parameters:
-        - in: body
-          name: "Object"
-          required: true
-          schema:
-            type: object
-      responses:
-        200:
-          description: "Successful echo of request data"

src/data-hub/agent-discovery-microservice/app/main.py

-import connexion
-
-# load swagger config
-app = connexion.App(__name__, specification_dir='configs/')
-app.add_api('swagger.yml')
-
-@app.route('/', methods=['GET'])
-def api_root():
-    return 'Endpoint of agent-discovery-microservice!'
-
-# start app
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=True)

src/data-hub/agent-discovery-microservice/deployment/deployment.yml

-apiVersion: v1
-kind: Service
-metadata:
-  name: agent-discovery
-spec:
-  type: LoadBalancer
-  selector:
-      app: agent-discovery
-  ports:
-    - name: http
-      port: 80
-      targetPort: 5000
-      nodePort: 30104
-      protocol: TCP
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: agent-discovery
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: agent-discovery
-  template:
-    metadata:
-      labels:
-        app: agent-discovery
-    spec:
-      containers:
-      - name: agent-discovery
-        image: alexx882/agent-discovery-microservice
-        ports:
-        - containerPort: 5000
\ No newline at end of file

src/data-hub/community-detection-microservice/app/configs/clustering.yaml

-layers:
-    user:
-        properties:
-    
-    starting-point:
-        properties:
-            - Latitude_StartingPoint
-            - Longitude_StartingPoint
\ No newline at end of file

src/data-hub/community-detection-microservice/app/create_user_graphs.py

-import sys
-import os
-modules_paths = ['../../../modules/']
-for modules_path in modules_paths:
-    if os.path.exists(modules_path):
-        sys.path.insert(1, modules_path)
-
-
-from typing import List, Tuple, Any
-from networkx import Graph
-
-from db.entities import LocationCluster, UserClusterGraph, Cluster
-from db.repository import Repository
-from processing.user_graph_generator import UserGraphGenerator
-
-
-repo = Repository()
-
-
-def get_edges_with_weights(g: Graph) -> List[Tuple[Any, Any, int]]:
-    res = []
-    for e in g.edges:
-        res.append((*e, g.edges[e]['weight']))
-    return res
-
-
-def create_graphs_for_location_clusters():
-    graphs_for_clusters = []
-
-    ug = UserGraphGenerator()
-    clusters: Cluster = repo.get_location_clusters()
-
-    for cluster in clusters:
-        user_ids = [n['user'] for n in cluster.nodes]
-        graph: Graph = ug.create_graph_from_nodes(user_ids)
-
-        vertices = list(graph.nodes)
-        edges = get_edges_with_weights(graph)
-
-        cluster_graph = UserClusterGraph(vertices, edges)
-        graphs_for_clusters.append(cluster_graph)
-
-    store_graphs(graphs_for_clusters)
-
-
-def store_graphs(graphs: List):
-    for g in graphs:
-        repo.add_user_cluster_graph(g)
-
-
-if __name__ == "__main__":
-    create_graphs_for_location_clusters()

src/data-hub/community-detection-microservice/app/db/agi/agi_repository.py

-import json
-from typing import List, Dict
-import hashlib
-
-class AgiRepository:
-    def getLocations(self) -> List[Dict]:
-        locations = []
-
-        travels = self.readDataFromFile()
-
-        # only take started travels
-        travels = [t for t in travels if t['status'] >= 2]
-        for travel in travels:
-
-            num_complete_travels = min(len(travel['startedBy']), len(travel['users']))
-            for i in range(num_complete_travels):
-
-                cur_location = travel['startedBy'][i]
-                cur_user = travel['users'][i]
-
-                locations.append(
-                    self.location(f'{travel["id"]}-{cur_location["moment"]}',
-                            cur_location['coordinate']['latitude'],
-                            cur_location['coordinate']['longitude'],
-                            cur_location['moment'],
-                            # todo user in travel startedBy not available from dataset - currently using user list 
-                            hashlib.sha1(cur_user['userId'].encode()).hexdigest() # not showing generated username 
-                ))
-
-        return locations
-
-    def getLocationsBasedOnNewDataSchema(self):
-        '''Creates the new data generic schema to be used beginning on 24.03.2020'''
-        data = {
-            'layer_name': 'Destination',
-            'nodes': self.getLocations(),
-            'properties': ['latitude', 'longitude']
-        }
-        return data
-        
-    def getTimesBasedOnNewDataSchema(self):
-        '''Creates the new data generic schema to be used beginning on 24.03.2020'''
-        data = {
-            'layer_name': 'Starting_Time',
-            'nodes': self.getLocations(),
-            'properties': ['timestamp']
-        }
-        return data
-
-    def readDataFromFile(self) -> List[Dict]:
-        with open('./db/agi/travels.json', 'r') as f_travels:
-            travels = json.loads(f_travels.read())
-
-        return travels
-
-    def location(self, id_, lat, long_, timestamp, username) -> dict:
-        return {
-            "id": id_,
-            'latitude': lat,
-            'longitude': long_,
-            "timestamp": timestamp,
-            "user": username
-        }
-

src/data-hub/community-detection-microservice/app/insert_agi_locations.py

-import sys
-import os
-modules_path = '../../../modules/'
-if os.path.exists(modules_path):
-    sys.path.insert(1, modules_path)
-
-from db.repository import Repository
-
-def insert_locations():
-    repo = Repository()
-
-    locs = repo.get_agi_locations()
-    for l in locs:
-        repo.add_location(l)
-
-if __name__ == "__main__":
-    insert_locations()    

src/data-hub/community-detection-microservice/app/processing/clustering/clustering_config.py

-import yaml
-from typing import Generator
-from pathlib import Path
-
-### init logging ###
-import logging
-LOG_FORMAT = (
-    '%(levelname) -5s %(asctime)s %(name)s:%(funcName) -35s %(lineno) -5d:  %(message)s')
-logging.basicConfig(level=logging.INFO, format=LOG_FORMAT)
-LOGGER = logging.getLogger(__name__)
-
-PROJECT_ROOT = Path(__file__).parent.parent.parent
-
-class ClusteringConfig:
-    '''Contains the configuration for the clustering algorithm defined in configs/clustering.yaml.'''
-
-    config_path = f'{PROJECT_ROOT}/configs/clustering.yaml'
-    config: dict = None
-
-    def __init__(self):
-        self.config = self._load_config()
-
-    def _load_config(self) -> dict:
-        '''Loads the whole configuration from file.'''
-        config = None
-
-        with open(self.config_path, 'r') as stream:
-            try:
-                config = yaml.safe_load(stream)
-            except yaml.YAMLError as exc:
-                LOGGER.error(exc)
-                config = {}
-
-        return config
-
-    def get_config(self):
-        return self.config
-
-    def get_layer_configs(self) -> Generator[dict, None, None]:
-        """
-        Returns a generator for the individual layer configs.
-        
-        Layer configs are dicts including a layer-name.
-        """
-        for key, layer in self.config['layers'].items():
-            layer['layer-name'] = key
-            yield layer

src/data-hub/community-detection-microservice/app/processing/user_graph_generator.py

-import itertools
-from typing import List, Dict, Tuple, Any
-from networkx import Graph
-
-
-class UserGraphGenerator:
-
-    def __init__(self):
-        pass
-
-    def count_edges(self, nodes: List) -> Dict[Tuple, int]:
-        edge_counts = {}
-
-        coms = itertools.combinations(nodes, 2)
-        for first, second in coms:
-            if first == second:  # dont process reflexive connections
-                continue
-
-            if (first, second) in edge_counts:
-                edge_counts[first, second] += 1
-            else:
-                edge_counts[first, second] = 1
-
-        return edge_counts
-
-    def create_edges_with_weights(self, edge_counts: Dict[Tuple[Any, Any], int]) -> List[Tuple[Any, Any, Dict]]:
-        edges = []
-        for (key1, key2), value in edge_counts.items():
-            edge = (key1, key2, {'weight': value})
-            edges.append(edge)
-
-        return edges
-
-    def create_fully_connected_edges_for_nodes(self, nodes: List) -> List[Tuple[Any, Any, Dict]]:
-        return self.create_edges_with_weights(self.count_edges(nodes))
-
-    def create_graph_from_nodes(self, nodes: List) -> Graph:
-        '''Creates a networkx.Graph with distinct nodes and weighted edges between these nodes'''
-        g = Graph()
-        g.add_nodes_from(nodes)
-        g.add_edges_from(self.create_fully_connected_edges_for_nodes(nodes))
-        return g

src/data-hub/community-detection-microservice/app/routes/cluster.py

-import io
-from flask import request, Response
-from db.repository import Repository
-from processing.clustering.clusterer import Clusterer
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-
-repo = Repository()
-clusterer = Clusterer()
-
-def get_locations():
-    clusters = repo.get_location_clusters()
-    return [c.to_serializable_dict() for c in clusters]
-
-def get_times():
-    clusters = repo.get_time_clusters()
-    return [c.to_serializable_dict() for c in clusters]
-
-def get_image_1():
-    return Response(status=501)
-    # todo
-
-    locations = repo.getLocations()
-
-    fig = clusterer.draw_locations(locations)
-
-    output = io.BytesIO()
-    FigureCanvas(fig).print_png(output)
-    return Response(output.getvalue(), mimetype="image/png")
-
-
-def get_image_2():
-    return Response(status=501)
-    # todo
-
-    locations = repo.getLocations()
-
-    fig = clusterer.draw_locations(locations)
-
-    output = io.BytesIO()
-    FigureCanvas(fig).print_png(output)
-    return Response(output.getvalue(), mimetype="image/png")
\ No newline at end of file

src/data-hub/community-detection-microservice/app/routes/functions.py

-import insert_agi_locations 
-import run_clustering
-import create_user_graphs
-
-def run_agi_clustering_and_graph_creation():
-    insert_agi_locations.insert_locations()
-    run_clustering.run_location_clustering()
-    run_clustering.run_time_clustering()
-    create_user_graphs.create_graphs_for_location_clusters()

src/data-hub/community-detection-microservice/app/routes/location.py

-from flask import request, Response
-from db.repository import Repository
-from db.entities import Location
-
-repo = Repository()
-
-def post():
-    body = request.json
-    _insert_location(body)
-    return Response(status=201)
-
-def post_many():
-    body = request.json
-    for location in body:
-        _insert_location(location)
-    return Response(status=201)
-
-def get():
-    return [l.to_serializable_dict() for l in repo.get_locations()]
-
-def _insert_location(location_data: dict):
-    repo.add_location(Location(location_data))

src/data-hub/community-detection-microservice/app/routes/user_cluster.py

-from flask import request, Response
-from db.repository import Repository
-
-repo = Repository()
-
-def get():
-    data = repo.get_user_cluster_graphs()
-    return [d.to_serializable_dict() for d in data]

src/data-hub/community-detection-microservice/app/tests/test_cluster.py

-import unittest
-import sys
-sys.path.insert(1, '../')
-
-# python -m unittest discover
-from db.entities.cluster import Cluster
-from db.entities import TimeCluster, LocationCluster
-
-from datetime import date, datetime
-import json
-
-
-class TestCluster(unittest.TestCase):
-    def test_init_Cluster(self):
-        c = Cluster(1, [1, 2, 3])
-
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-
-class TestLocationCluster(unittest.TestCase):
-    def setUp(self):
-        self.c = LocationCluster(1, [1, 2, 3])
-
-    def test_init_individualArguments(self):
-        c = LocationCluster(1, [1, 2, 3])
-
-        self.assertEqual('1', c.id)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_init_dictArgument(self):
-        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': [1, 2, 3]}
-        c = LocationCluster(location_dict=dict_)
-
-        self.assertEqual('123', c.id)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_init_dictArgument_fromDb(self):
-        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': '[1, 2, 3]'}
-        c = LocationCluster(location_dict=dict_, from_db=True)
-
-        self.assertEqual('123', c.id)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_to_serializable_dict_noDb(self):
-        c_dict = self.c.to_serializable_dict()
-
-        self.assertEqual(self.c.id, c_dict['id'])
-        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
-        self.assertEqual(self.c.nodes, c_dict['nodes'])
-
-    def test_from_serializable_dict_noDb(self):
-        new_c = LocationCluster()
-        new_c.from_serializable_dict(self.c.to_serializable_dict())
-
-        self.assertEqual(self.c.id, new_c.id)
-        self.assertEqual(str(self.c), str(new_c))
-
-    def test_to_serializable_dict_db_jsonNodes(self):
-        c_dict = self.c.to_serializable_dict(for_db=True)
-
-        self.assertEqual(self.c.id, c_dict['id'])
-        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
-        self.assertEqual(self.c.nodes, json.loads(c_dict['nodes']))
-
-    def test_from_serializable_dict_fromDb(self):
-        new_c = LocationCluster()
-        new_c.from_serializable_dict(
-            self.c.to_serializable_dict(for_db=True), from_db=True)
-
-        self.assertEqual(self.c.id, new_c.id)
-        self.assertEqual(str(self.c), str(new_c))
-
-
-class TestTimeCluster(unittest.TestCase):
-
-    def setUp(self):
-        self.date_ = date(2020, 1, 1)
-        self.c = TimeCluster(self.date_, 14, 1, [1, 2, 3])
-
-    def test_init_individualArguments(self):
-        c = TimeCluster(self.date_, 14, 1, [1, 2, 3])
-
-        self.assertEqual(f'{self.date_}-14-1', c.id)
-        self.assertEqual(self.date_, c.date)
-        self.assertEqual(14, c.hour)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_init_dictArgument(self):
-        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': [1, 2, 3],
-                 'date': str(self.date_), 'hour': 14}
-        c = TimeCluster(time_dict=dict_)
-
-        self.assertEqual('123', c.id)
-        self.assertEqual(self.date_, c.date)
-        self.assertEqual(14, c.hour)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_init_dictArgument_fromDb(self):
-        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': '[1, 2, 3]',
-                 'date': str(self.date_), 'hour': 14}
-        c = TimeCluster(time_dict=dict_, from_db=True)
-
-        self.assertEqual('123', c.id)
-        self.assertEqual(self.date_, c.date)
-        self.assertEqual(14, c.hour)
-        self.assertEqual(1, c.cluster_label)
-        self.assertEqual([1, 2, 3], c.nodes)
-
-    def test_to_serializable_dict_noDb(self):
-        c_dict = self.c.to_serializable_dict()
-
-        self.assertEqual(self.c.id, c_dict['id'])
-        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
-        self.assertEqual(self.c.nodes, c_dict['nodes'])
-        self.assertEqual(self.c.date, datetime.strptime(
-            c_dict['date'], '%Y-%m-%d').date())
-        self.assertEqual(self.c.hour, c_dict['hour'])
-
-    def test_from_serializable_dict_noDb(self):
-        new_c = TimeCluster()
-        new_c.from_serializable_dict(self.c.to_serializable_dict())
-
-        self.assertEqual(self.c.id, new_c.id)
-        self.assertEqual(str(self.c), str(new_c))
-
-    def test_to_serializable_dict_fromDb_jsonNodes(self):
-        c_dict = self.c.to_serializable_dict(for_db=True)
-
-        self.assertEqual(self.c.id, c_dict['id'])
-        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
-        self.assertEqual(self.c.nodes, json.loads(c_dict['nodes']))
-        self.assertEqual(self.c.date, datetime.strptime(
-            c_dict['date'], '%Y-%m-%d').date())
-        self.assertEqual(self.c.hour, c_dict['hour'])
-
-    def test_from_serializable_dict_fromDb(self):
-        new_c = TimeCluster()
-        new_c.from_serializable_dict(
-            self.c.to_serializable_dict(for_db=True), from_db=True)
-
-        self.assertEqual(self.c.id, new_c.id)
-        self.assertEqual(str(self.c), str(new_c))
-
-
-if __name__ == '__main__':
-    unittest.main()

src/data-hub/community-detection-microservice/app/tests/test_clustering_config.py

-import unittest
-import sys
-for path in ['../', './']:
-    sys.path.insert(1, path)
-
-# python -m unittest discover
-from processing.clustering.clustering_config import ClusteringConfig
-
-class TestClusteringConfig(unittest.TestCase):
-    
-    def setUp(self):
-        self.clustering_config = ClusteringConfig()
-
-    def test_get_layer_configs_noneInput_noneOutput(self):
-        for layer_config in self.clustering_config.get_layer_configs():
-            self.assertIn('layer-name', layer_config)
-
-if __name__ == '__main__':
-    unittest.main()

src/data-hub/community-detection-microservice/app/tests/test_user_graph_generator.py

-import unittest
-import sys
-for path in ['../', './']:
-    sys.path.insert(1, path)
-
-# python -m unittest discover
-from processing.user_graph_generator import UserGraphGenerator
-import networkx as nx
-
-
-class TestUserGraphGenerator(unittest.TestCase):
-
-    def setUp(self):
-        self.user_graph = UserGraphGenerator()
-
-    def test_count_edges_oneNode(self):
-        count_res = {}
-        self.assertEqual(count_res, self.user_graph.count_edges([1]))
-
-    def test_count_edges_threeDistinctNodes_threeEdges(self):
-        count_res = {(1, 2): 1, (1, 3): 1, (2, 3): 1}
-        self.assertEqual(count_res, self.user_graph.count_edges([1, 2, 3]))
-
-    def test_count_edges_twoNodesWithDups_notReflexive(self):
-        count_res = {}
-        self.assertEqual(count_res, self.user_graph.count_edges([1, 1]))
-
-    def test_count_edges_threeNodesWithDups_countGtOne_notReflexive(self):
-        count_res = {(1, 3): 2}
-        self.assertEqual(count_res, self.user_graph.count_edges([1, 1, 3]))
-
-    def test_count_edges_fourNodesWithDups_countGtOne_notReflexive(self):
-        count_res = {(1, 3): 2, (1, 4): 2, (3, 4): 1}
-        self.assertEqual(count_res, self.user_graph.count_edges([1, 1, 3, 4]))
-
-    def test_count_edges_fourStringNodesWithDups_countGtOne_notReflexive(self):
-        count_res = {('test', 'test2'): 2, ('test', '4'): 2, ('test2', '4'): 1}
-        self.assertEqual(count_res,
-                         self.user_graph.count_edges(['test', 'test', 'test2', '4']))
-
-    def test_count_edges_fourDistinctStringNodes_fullyConnectedEdges(self):
-        count_res = {
-            ('1', '2'): 1, ('1', '3'): 1, ('1', '4'): 1,
-            ('2', '3'): 1, ('2', '4'): 1, ('3', '4'): 1
-        }
-        self.assertEqual(count_res,
-                         self.user_graph.count_edges(['1', '2', '3', '4']))
-
-    def test_create_edges_with_weights_SingleEdge(self):
-        counts = {('a', 'b'): 1}
-        edge_result = [('a', 'b', {'weight': 1})]
-        self.assertEqual(edge_result,
-                         self.user_graph.create_edges_with_weights(counts))
-
-    def test_create_edges_with_weights_SingleEdgeWeightTwo(self):
-        counts = {('a', 'b'): 2}
-        edge_result = [('a', 'b', {'weight': 2})]
-        self.assertEqual(edge_result,
-                         self.user_graph.create_edges_with_weights(counts))
-
-    def test_create_edges_with_weights_TwoEdgesWithWeights(self):
-        counts = {('a', 'b'): 2, ('b', 'c'): 1}
-        edge_result = [('a', 'b', {'weight': 2}), ('b', 'c', {'weight': 1})]
-        self.assertEqual(edge_result,
-                         self.user_graph.create_edges_with_weights(counts))
-
-    def test_create_graph_from_nodes_singleNode(self):
-        nodes = [1]
-        edges = []
-        self.assertGraph(nodes, edges,
-                         self.user_graph.create_graph_from_nodes(nodes))
-
-    def test_create_graph_from_nodes_twoDistinctNodes(self):
-        nodes = [1, 2]
-        edges = [(1, 2, {'weight': 1})]
-        self.assertGraph(nodes, edges,
-                         self.user_graph.create_graph_from_nodes(nodes))
-
-    def test_create_graph_from_nodes_threeDistinctNodes(self):
-        nodes = [1, 2, 3]
-        edges = [(1, 2, {'weight': 1}), (1, 3, {'weight': 1}),
-                 (2, 3, {'weight': 1})]
-        self.assertGraph(nodes, edges,
-                         self.user_graph.create_graph_from_nodes(nodes))
-
-    def test_create_graph_from_nodes_threeNodesWithDuplicates_TwoNodes_EdgesWithAccordingWeight(self):
-        nodes = [1, 1, 3]
-        edges = [(1, 3, {'weight': 2})]
-        self.assertGraph(list(set(nodes)), edges,
-                         self.user_graph.create_graph_from_nodes(nodes))
-
-    # unittest custom assertions
-    def assertGraph(self, nodes, edges, g: nx.Graph):
-        self.assertEqual(len(nodes), g.number_of_nodes())
-        self.assertEqual(len(edges), g.number_of_edges())
-
-        for i in range(len(nodes)):
-            self.assertEqual(nodes[i], list(g.nodes)[i])
-
-        for i in range(len(edges)):
-            graph_edge = list(g.edges)[i]
-            first, second, weight = edges[i]
-
-            self.assertEqual((first, second), graph_edge)
-            self.assertEqual(weight, g.edges[graph_edge])
-
-
-if __name__ == '__main__':
-    unittest.main()

src/data-hub/community-detection-microservice/app/visualization/visualize_time_slices.py

-import sys
-import os
-for path in ['../', './', '../../../modules/']:
-    if os.path.exists(path):
-        sys.path.insert(1, path)
-
-import matplotlib.pyplot as plt
-from db.repository import Repository
-from db.entities import TimeSlice
-from typing import List
-
-
-def plt_show_circles(time_slices: List[TimeSlice], cluster_no):
-    cluster_no = str(cluster_no)
-
-    for slice_ in time_slices:
-
-        nodes = slice_.get_nodes_for_cluster(cluster_no)
-            
-        # print(f"{slice_.time} number elements for cluster {cluster_no}: {len(nodes)}")
-
-        plt.title(str(slice_.time))
-
-        plt.scatter([n['Longitude_Destination'] if 'Longitude_Destination' in n else 0
-                        for n in nodes],
-                    [n['Latitude_Destination'] if 'Latitude_Destination' in n else 0
-                        for n in nodes],
-                    s=[len(nodes)*100]*len(nodes))
-
-        plt.pause(0.5)
-
-
-def plt_show_bars(time_slices: List[TimeSlice], cluster_no):
-    cluster_no = str(cluster_no)
-    
-    labels = [ts.time for ts in time_slices]  
-    x_axis_label_stepsize = 10  
-
-    nodes_per_slice_for_single_cluster = \
-            [len(time_slice.get_nodes_for_cluster(cluster_no))
-            for time_slice
-            in time_slices]
-
-    fig, ax = plt.subplots()
-    ax.bar(x=range(len(labels)),
-        height=nodes_per_slice_for_single_cluster)
-
-    ax.set_ylabel('Size')
-    ax.set_title(f'Cluster-{cluster_no} size over time')
-    ax.set_xticks(range(len(labels))[::x_axis_label_stepsize])
-    ax.set_xticklabels(labels[::x_axis_label_stepsize])
-
-    plt.show()
-
-
-if __name__ == "__main__":
-    repo = Repository()
-    time_slices = repo.get_time_slices_by_name("Destination_Layer")
-
-    # chronological order
-    time_slices.sort(key=lambda ts: eval(ts.time))
-
-    print(len(time_slices))
-    plt_show_bars(time_slices, cluster_no = 0)
\ No newline at end of file

src/data-hub/democratic-reasoning-microservice/Dockerfile

-FROM python:3
-LABEL maintainer="Alexander Lercher"
-
-ENV http_proxy http://proxy.uni-klu.ac.at:3128/
-ENV https_proxy http://proxy.uni-klu.ac.at:3128/
-
-RUN apt-get update
-RUN pip install flask
-RUN pip install connexion[swagger-ui]
-
-EXPOSE 5000
-
-WORKDIR /app
-COPY src/data-hub/democratic-reasoning-microservice/app/ /app/
-RUN chmod a+x main.py
-
-CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/democratic-reasoning-microservice/README.md

-# Democratic Reasoning Microservice
-The democratic reasoning microservice serves as a central knowledge-based component providing all facts and rules for other microservices. 
-
-## Technologies
-- Python 3.x
-- Python module Flask
-- Python module Connexion with Swagger
-- Docker
-- Kubernetes
\ No newline at end of file

src/data-hub/democratic-reasoning-microservice/app/debug.py

-from flask import request
-
-def echo():
-    return request.json
-
- 
\ No newline at end of file

src/data-hub/democratic-reasoning-microservice/app/main.py

-import connexion
-
-# load swagger config
-app = connexion.App(__name__, specification_dir='configs/')
-app.add_api('swagger.yml')
-
-@app.route('/', methods=['GET'])
-def api_root():
-    return 'Endpoint of democratic-reasoning-microservice!'
-
-# start app
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=True)

src/data-hub/democratic-reasoning-microservice/deployment/deployment.yml

-apiVersion: v1
-kind: Service
-metadata:
-  name: democratic-reasoning
-spec:
-  type: LoadBalancer
-  selector:
-      app: democratic-reasoning
-  ports:
-    - name: http
-      port: 80
-      targetPort: 5000
-      nodePort: 30106
-      protocol: TCP
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: democratic-reasoning
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: democratic-reasoning
-  template:
-    metadata:
-      labels:
-        app: democratic-reasoning
-    spec:
-      containers:
-      - name: democratic-reasoning
-        image: alexx882/democratic-reasoning-microservice
-        ports:
-        - containerPort: 5000
\ No newline at end of file

src/data-hub/geo-profiling-microservice/Dockerfile

-FROM python:3
-LABEL maintainer="Alexander Lercher"
-
-ENV http_proxy http://proxy.uni-klu.ac.at:3128/
-ENV https_proxy http://proxy.uni-klu.ac.at:3128/
-
-RUN apt-get update
-RUN pip install flask
-RUN pip install connexion[swagger-ui]
-
-EXPOSE 5000
-
-WORKDIR /app
-COPY src/data-hub/geo-profiling-microservice/app/ /app/
-RUN chmod a+x main.py
-
-CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/geo-profiling-microservice/README.md

-# Geo Profiling Microservice
-The geo profiling microservice helps to cluster users physically close to each other. 
-
-## Technologies
-- Python 3.x
-- Python module Flask
-- Python module Connexion with Swagger
-- Docker
-- Kubernetes
\ No newline at end of file

src/data-hub/geo-profiling-microservice/app/configs/swagger.yml

-swagger: "2.0"
-info:
-  title: Geo Profiling microservice
-  description: This is the documentation for the geo profiling microservice.
-  version: "1.0.0"
-
-consumes:
-  - "application/json"
-produces:
-  - "application/json"
-
-basePath: "/api"
-
-paths:
-  /debug:
-    post:
-      operationId: "debug.echo"
-      tags:
-        - "Echo"
-      summary: "Echo function for debugging purposes"
-      description: "Echoes the input back to the caller."
-      parameters:
-        - in: body
-          name: "Object"
-          required: true
-          schema:
-            type: object
-      responses:
-        200:
-          description: "Successful echo of request data"

src/data-hub/geo-profiling-microservice/app/debug.py

-from flask import request
-
-def echo():
-    return request.json
-
- 
\ No newline at end of file

src/data-hub/geo-profiling-microservice/deployment/deployment.yml

-apiVersion: v1
-kind: Service
-metadata:
-  name: geo-profiling
-spec:
-  type: LoadBalancer
-  selector:
-      app: geo-profiling
-  ports:
-    - name: http
-      port: 80
-      targetPort: 5000
-      nodePort: 30105
-      protocol: TCP
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: geo-profiling
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: geo-profiling
-  template:
-    metadata:
-      labels:
-        app: geo-profiling
-    spec:
-      containers:
-      - name: geo-profiling
-        image: alexx882/geo-profiling-microservice
-        ports:
-        - containerPort: 5000
\ No newline at end of file

src/data-hub/agent-discovery-microservice/Dockerfile → src/data-hub/proactive-community-detection-microservice/Dockerfile

@@ -11,7 +11,8 @@ RUN pip install connexion[swagger-ui]
 EXPOSE 5000
 
 WORKDIR /app
-COPY src/data-hub/agent-discovery-microservice/app/ /app/
+
+COPY src/data-hub/proactive-community-detection-microservice/app/ /app/
 RUN chmod a+x main.py
 
 CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/community-detection-microservice/README.md → src/data-hub/proactive-community-detection-microservice/README.md

-# Community Detection Microservice
+# Proactive Community Detection Microservice
 The community detection microservice helps in deeper network understand-
 ing and reveals interesting properties shared by the members. We propose a novel approach that combines event clustering and link analysis to detect communities along with clustering users into overlapping communities via agent, role, stage discovery microservices.
 

src/data-hub/democratic-reasoning-microservice/app/configs/swagger.yml → src/data-hub/proactive-community-detection-microservice/app/configs/swagger.yml

 swagger: "2.0"
 info:
-  title: Democratic Reasoning microservice
-  description: This is the documentation for the democratic reasoning microservice.
+  title: Proactive Community Detection microservice
+  description: This is the documentation for the proactive community detection microservice.
   version: "1.0.0"
 
 consumes:

src/data-hub/geo-profiling-microservice/app/main.py → src/data-hub/proactive-community-detection-microservice/app/main.py

@@ -6,7 +6,7 @@ app.add_api('swagger.yml')
 
 @app.route('/', methods=['GET'])
 def api_root():
-    return 'Endpoint of geo-profiling-microservice!'
+    return 'Endpoint of proactive-community-detection-microservice!'
 
 # start app
 if __name__ == '__main__':

src/data-hub/proactive-community-detection-microservice/deployment/deployment.yml

+apiVersion: v1
+kind: Service
+metadata:
+  name: proactive-community-detection
+spec:
+  type: LoadBalancer
+  selector:
+      app: proactive-community-detection
+  ports:
+    - name: http
+      port: 80
+      targetPort: 5000
+      nodePort: 30105
+      protocol: TCP
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: proactive-community-detection
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: proactive-community-detection
+  template:
+    metadata:
+      labels:
+        app: proactive-community-detection
+    spec:
+      containers:
+      - name: proactive-community-detection
+        image: alexx882/proactive-community-detection-microservice
+        ports:
+        - containerPort: 5000
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: proactive-community-detection-db
+spec:
+  type: LoadBalancer
+  selector:
+      app: proactive-community-detection-db
+  ports:
+    - name: http
+      port: 27017
+      targetPort: 27017
+      nodePort: 30106
+      protocol: TCP
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: proactive-community-detection-db
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: proactive-community-detection-db
+  template:
+    metadata:
+      labels:
+        app: proactive-community-detection-db
+    spec:
+      containers:
+      - name: proactive-community-detection-db
+        image: mongo
+        env:
+        - name: MONGO_INITDB_ROOT_USERNAME
+          value: root
+        - name: MONGO_INITDB_ROOT_PASSWORD
+          value: root
+        ports:
+        - containerPort: 27017
\ No newline at end of file

src/data-hub/role-discovery-microservice/Dockerfile

-FROM python:3
-LABEL maintainer="Alexander Lercher"
-
-ENV http_proxy http://proxy.uni-klu.ac.at:3128/
-ENV https_proxy http://proxy.uni-klu.ac.at:3128/
-
-RUN apt-get update
-RUN pip install flask
-RUN pip install connexion[swagger-ui]
-
-EXPOSE 5000
-
-WORKDIR /app
-COPY src/data-hub/role-discovery-microservice/app/ /app/
-RUN chmod a+x main.py
-
-CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/role-discovery-microservice/README.md

-# Role Discovery Microservice
-The role discovery microservice labels anonymous users with individual roles which are important for classification, e.g. the role father in the concept family.
-
-## Technologies
-- Python 3.x
-- Python module Flask
-- Python module Connexion with Swagger
-- Docker
-- Kubernetes
\ No newline at end of file

src/data-hub/role-discovery-microservice/app/configs/swagger.yml

-swagger: "2.0"
-info:
-  title: Role Discovery microservice
-  description: This is the documentation for the role discovery microservice.
-  version: "1.0.0"
-
-consumes:
-  - "application/json"
-produces:
-  - "application/json"
-
-basePath: "/api"
-
-paths:
-  /debug:
-    post:
-      operationId: "debug.echo"
-      tags:
-        - "Echo"
-      summary: "Echo function for debugging purposes"
-      description: "Echoes the input back to the caller."
-      parameters:
-        - in: body
-          name: "Object"
-          required: true
-          schema:
-            type: object
-      responses:
-        200:
-          description: "Successful echo of request data"

src/data-hub/role-discovery-microservice/app/debug.py

-from flask import request
-
-def echo():
-    return request.json
-
- 
\ No newline at end of file

src/data-hub/role-discovery-microservice/app/main.py

-import connexion
-
-# load swagger config
-app = connexion.App(__name__, specification_dir='configs/')
-app.add_api('swagger.yml')
-
-@app.route('/', methods=['GET'])
-def api_root():
-    return 'Endpoint of role-discovery-microservice!'
-
-# start app
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=True)

src/data-hub/role-discovery-microservice/deployment/deployment.yml

-apiVersion: v1
-kind: Service
-metadata:
-  name: role-discovery
-spec:
-  type: LoadBalancer
-  selector:
-      app: role-discovery
-  ports:
-    - name: http
-      port: 80
-      targetPort: 5000
-      nodePort: 30102
-      protocol: TCP
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: role-discovery
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: role-discovery
-  template:
-    metadata:
-      labels:
-        app: role-discovery
-    spec:
-      containers:
-      - name: role-discovery
-        image: alexx882/role-discovery-microservice
-        ports:
-        - containerPort: 5000
\ No newline at end of file

src/data-hub/community-detection-microservice/Dockerfile → src/data-hub/role-stage-discovery-microservice/Dockerfile

@@ -11,11 +11,11 @@ EXPOSE 5000
 WORKDIR /app
 
 # https://www.aptible.com/documentation/faq/deploy/dockerfile-caching/pip-dockerfile-caching.html
-COPY src/data-hub/community-detection-microservice/app/requirements.txt /app/
+COPY src/data-hub/role-stage-discovery-microservice/app/requirements.txt /app/
 RUN pip install -r requirements.txt
 
 COPY src/modules/ /app/
-COPY src/data-hub/community-detection-microservice/app/ /app/
+COPY src/data-hub/role-stage-discovery-microservice/app/ /app/
 RUN chmod a+x main.py
 
 CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/README.md

+# Role Stage Discovery Microservice
+The role stage discovery microservice detects communities, their roles based on contextualized features and stages of roles over time.
+
+## Technologies
+- Python 3.x
+- Docker
+- Kubernetes
\ No newline at end of file

src/data-hub/community-detection-microservice/app/db/entities/__init__.py → src/data-hub/role-stage-discovery-microservice/app/db/entities/__init__.py

 from db.entities.location import Location
 from db.entities.popular_location import PopularLocation
-from db.entities.cluster import Cluster, LocationCluster, TimeCluster
+from db.entities.cluster import Cluster
 from db.entities.clusterset import ClusterSet
 from db.entities.user_cluster_graph import UserClusterGraph
 from db.entities.layer import Layer

src/data-hub/community-detection-microservice/app/db/entities/cluster.py → src/data-hub/role-stage-discovery-microservice/app/db/entities/cluster.py

@@ -39,67 +39,3 @@ class Cluster:
 
     def __str__(self):
         return f"Cluster({self.__repr__()})"
-
-
-class LocationCluster(Cluster):
-    def __init__(self, cluster_label: int = None, nodes: List = None,
-                 location_dict: Dict = None, from_db=False):
-        super().__init__(cluster_label, nodes)
-        self.id = f'{self.cluster_label}'
-
-        if location_dict is not None:
-            self.from_serializable_dict(location_dict, from_db)
-
-    def to_serializable_dict(self, for_db=False) -> Dict:
-        return {
-            "id": self.id,
-            "cluster_label": self.cluster_label,
-            "nodes": json.dumps(self.nodes) if for_db else self.nodes
-        }
-
-    def from_serializable_dict(self, location_dict: Dict, from_db=False):
-        self.id = location_dict["id"]
-        self.cluster_label = location_dict["cluster_label"]
-        self.nodes = json.loads(location_dict["nodes"]) \
-            if from_db else location_dict["nodes"]
-
-    def __repr__(self):
-        return json.dumps(self.to_serializable_dict())
-
-    def __str__(self):
-        return f"LocationCluster({self.__repr__()})"
-
-
-class TimeCluster(Cluster):
-    def __init__(self, date: date = None, hour: int = None, cluster_label: int = None, nodes: List = None,
-                 time_dict: Dict = None, from_db=False):
-        super().__init__(cluster_label, nodes)
-        self.date = date
-        self.hour = hour
-        self.id = f'{self.date}-{self.hour}-{self.cluster_label}'
-
-        if time_dict is not None:
-            self.from_serializable_dict(time_dict, from_db)
-
-    def to_serializable_dict(self, for_db=False) -> Dict:
-        return {
-            "id": self.id,
-            "date": str(self.date),
-            "hour": self.hour,
-            "cluster_label": self.cluster_label,
-            "nodes": json.dumps(self.nodes) if for_db else self.nodes
-        }
-
-    def from_serializable_dict(self, time_dict: Dict, from_db=False):
-        self.id = time_dict["id"]
-        self.date = datetime.strptime(time_dict["date"], '%Y-%m-%d').date()
-        self.hour = time_dict["hour"]
-        self.cluster_label = time_dict["cluster_label"]
-        self.nodes = json.loads(time_dict["nodes"]) \
-            if from_db else time_dict["nodes"]
-
-    def __repr__(self):
-        return json.dumps(self.to_serializable_dict(True))
-
-    def __str__(self):
-        return f"TimeCluster({self.__repr__()})"

src/data-hub/community-detection-microservice/app/db/repository.py → src/data-hub/role-stage-discovery-microservice/app/db/repository.py

@@ -3,8 +3,6 @@ import network_constants as netconst
 from database.MongoRepositoryBase import MongoRepositoryBase
 import json
 
-from db.agi.agi_repository import AgiRepository
-
 from db.entities import *
 from typing import List
 
@@ -13,61 +11,14 @@ class Repository(MongoRepositoryBase):
     '''This is a repository for MongoDb.'''
 
     def __init__(self):
-        super().__init__(netconst.COMMUNITY_DETECTION_DB_HOSTNAME,
-                         netconst.COMMUNITY_DETECTION_DB_PORT,
-                         'communityDetectionDb')
-
-        self._location_collection = 'location'
-        self._location_cluster_collection = 'location_cluster'
-        self._time_cluster_collection = 'time_cluster'
-        self._user_cluster_graph_collection = 'user_cluster_graph'
-        self._layer_collection = 'layer-new'
-        self._layer_nodes_collection = 'layer_nodes-new'
-        self._clusterset_collection = 'cluster_set-new'
-        self._time_slice_collection = 'time_slice-new'
-
-        self.agi_repo = AgiRepository()
-
-#region Location
-    def add_location(self, location: Location):
-        super().insert_entry(self._location_collection, location.to_serializable_dict())
-
-    def get_locations(self) -> List[Location]:
-        locations = super().get_entries(self._location_collection)
-        return [Location(l) for l in locations]
-
-    def get_agi_locations(self) -> List[Location]:
-        agi_locations = self.agi_repo.getLocations()
-        return [Location(agi_loc) for agi_loc in agi_locations]
-#endregion
-
-#region Specific Clusters
-    def add_location_cluster(self, cluster: LocationCluster):
-        super().insert_entry(self._location_cluster_collection,
-                             cluster.to_serializable_dict(for_db=True))
+        super().__init__(netconst.ROLESTAGE_DISCOVERY_DB_HOSTNAME,
+                         netconst.ROLESTAGE_DISCOVERY_DB_PORT,
+                         'roleStageDb')
 
-    def get_location_clusters(self) -> List[LocationCluster]:
-        clusters = super().get_entries(self._location_cluster_collection)
-        return [LocationCluster(location_dict=c, from_db=True) for c in clusters]
-
-    def add_time_cluster(self, cluster: TimeCluster):
-        super().insert_entry(self._time_cluster_collection,
-                             cluster.to_serializable_dict(for_db=True))
-
-    def get_time_clusters(self) -> List[TimeCluster]:
-        clusters = super().get_entries(self._time_cluster_collection)
-        return [TimeCluster(time_dict=c, from_db=True) for c in clusters]
-#endregion
-
-#region Cluster Graph
-    def add_user_cluster_graph(self, user_graph: UserClusterGraph):
-        super().insert_entry(self._user_cluster_graph_collection,
-                             user_graph.to_serializable_dict(for_db=True))
-
-    def get_user_cluster_graphs(self) -> List[UserClusterGraph]:
-        user_graphs = super().get_entries(self._user_cluster_graph_collection)
-        return [UserClusterGraph(dict_=u, from_db=True) for u in user_graphs]
-#endregion
+        self._layer_collection = 'layers'
+        self._layer_nodes_collection = 'layer_nodes'
+        self._clusters_collection = 'clusters'
+        self._time_slice_collection = 'time_slices'
 
 #region Layers
     def add_layer(self, layer: Layer):
@@ -77,10 +28,6 @@ class Repository(MongoRepositoryBase):
         entries = super().get_entries(self._layer_collection)
         return [Layer(e) for e in entries]
 
-    def get_layer_names(self) -> List[str]:
-        entries = super().get_entries(self._layer_collection, projection={'layer_name': 1})
-        return [e['layer_name'] for e in entries]
-
     def get_layer(self, layer_name) -> Layer:
         entries = super().get_entries(self._layer_collection, selection={'layer_name': layer_name})
         entries = [Layer(e) for e in entries]
@@ -103,38 +50,13 @@ class Repository(MongoRepositoryBase):
 
 #endregion
 
-#region ClusterSet
-# TODO cleanup
-    def add_clusterset(self, cluster_set: ClusterSet):
-        super().insert_entry(self._clusterset_collection, cluster_set.to_serializable_dict())
-
-    def get_clustersets(self) -> List[ClusterSet]:
-        '''Returns all clustersets.'''
-        entries = super().get_entries(self._clusterset_collection)
-        return [ClusterSet(cluster_set_dict=e) for e in entries]
-
-    def get_clusterset_names(self) -> List[str]:
-        '''Returns the names of all clustersets.'''
-        entries = super().get_entries(self._clusterset_collection, projection={'layer_name': 1})
-        return [e['layer_name'] for e in entries]
-
-    def get_clusterset(self, layer_name) -> ClusterSet:
-        '''Returns a single clusterset with the given name or None otherwise.'''
-        entries = super().get_entries(self._clusterset_collection, selection={'layer_name': layer_name})
-        entries = [ClusterSet(cluster_set_dict=e) for e in entries]
-
-        if entries is not None and len(entries) > 0:
-            return entries[0]
-        else:
-            return None
-    
-    
+#region Clusters
     def add_clusters(self, clusters: List[Cluster]):
         cluster_dicts = [c.to_serializable_dict(for_db=True) for c in clusters]
-        super().insert_many(self._clusterset_collection, cluster_dicts)
+        super().insert_many(self._clusters_collection, cluster_dicts)
         
     def get_clusters_for_layer(self, layer_name: str) -> List[Cluster]:
-        entries = super().get_entries(self._clusterset_collection, selection={'layer_name': layer_name}, projection={'_id': 0})
+        entries = super().get_entries(self._clusters_collection, selection={'layer_name': layer_name}, projection={'_id': 0})
         return [Cluster(cluster_dict=e, from_db=True) for e in entries]
     
 #endregion
@@ -155,4 +77,5 @@ class Repository(MongoRepositoryBase):
 
     def remove_all_time_slices(self):
         super().drop_collection(self._time_slice_collection)
+
 #endregion
\ No newline at end of file

src/data-hub/community-detection-microservice/app/main.py → src/data-hub/role-stage-discovery-microservice/app/main.py

@@ -20,7 +20,7 @@ app.add_api('swagger.yml')
 
 @app.route('/', methods=['GET'])
 def api_root():
-    return 'Endpoint of community-detection-microservice!'
+    return 'Endpoint of role-stage-discovery-microservice!'
 
 # start app
 if __name__ == '__main__':    

src/data-hub/community-detection-microservice/app/processing/clustering/clusterer.py → src/data-hub/role-stage-discovery-microservice/app/processing/clustering/clusterer.py

@@ -3,7 +3,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.cluster import OPTICS
 from typing import List, Dict, Any, TypeVar
-from deprecated import deprecated
 
 T = TypeVar('T')
 ClusterGroup = Dict[Any, List[Dict]]
@@ -16,65 +15,23 @@ class Clusterer:
     :param epsilon: Eps used in OPTICS 
     :param min_points: MinPts used in OPTICS
     '''
-    def __init__(self, epsilon=11, min_points=5):
-        self.epsilon = epsilon
+    def __init__(self, min_points=5):
         self.min_points = min_points
-
-    def draw_locations(self, locations:List, labels:List=None) -> plt.Figure:
-        if locations is None or len(locations) == 0:
-            return self._draw_locations()
-
-        if labels is None or len(locations) != len(labels):
-            labels = self.create_labels(locations)
-
-        return self._draw_locations(
-            locations = self.extract_location_data(locations),
-            partition_info = labels
-        )
-
-    def _draw_locations(self, locations:np.ndarray=None, centroids:np.ndarray=None, partition_info:List=None) -> plt.Figure:
-        fig = plt.Figure()
-        axis = fig.add_subplot(1, 1, 1)
-
-        if locations is not None:
-            colors = plt.cm.rainbow(np.linspace(0, 1, len(locations)))
-
-            if partition_info is not None:
-                distinct_colors = plt.cm.rainbow(np.linspace(0, 1, len(set(partition_info))))
-                colors = [distinct_colors[pi] for pi in partition_info]
-
-            # draw locations with random colors
-            axis.scatter(locations[:,0],
-                        locations[:,1], 
-                        c=colors)
-
-        if centroids is not None:
-            # draw black centroids
-            axis.scatter(centroids[:,0], centroids[:,1], c='k', marker='x', s=80)
-            
-        return fig
     
     def create_labels(self, features:np.ndarray) -> List[int]:
-        '''Creates labels for the items based on DBSCAN.'''
+        '''Creates labels for the items based on OPTICS.'''
         if features is None or len(features) == 0:
             return features # trash in trash out
     
-        dbsc = OPTICS(min_samples=self.min_points) # DBSCAN(eps = self.epsilon, min_samples = self.min_points)
-        dbsc = dbsc.fit(features)
-        labels = dbsc.labels_
+        optics = OPTICS(min_samples=self.min_points)
+        optics = optics.fit(features)
+        labels = optics.labels_
 
         return labels.tolist()
 
-    @deprecated(reason="Use generic version instead")
-    def extract_location_features(self, locations: List[dict]) -> np.ndarray:
-        return np.asarray([(float(l['latitude']), float(l['longitude'])) for l in locations])
-
-    @deprecated(reason="Use generic version instead")
-    def extract_time_features(self, times: List[Dict]) -> np.ndarray:
-        return np.asarray([[float(t['timestamp'])] for t in times])
-
     def _extract_features(self, dataset: List[Dict], features:List[str]) -> np.ndarray:
         '''Extracts the feature values from the dataset into a np array with same order as original dataset.'''
+        # TODO single input
         extracted_features = []
         for data in dataset:
             entry = [float(data[feature]) for feature in features]
@@ -82,7 +39,7 @@ class Clusterer:
 
         return np.asarray(extracted_features)
 
-    def label_dataset(self, dataset:List[Dict], labels:List[Any]) -> List:
+    def label_dataset(self, dataset:List[Dict], labels:List[Any]):
         '''Adds the labels to the elements of the dataset at the same position. The new key is called cluster_label.'''
         if dataset is None or labels is None:
             return
@@ -96,38 +53,12 @@ class Clusterer:
             dataset[i]['cluster_label'] = labels[i]
 
     def group_by_clusters(self, dataset:List[Dict], labels:List[Any]) -> ClusterGroup:
-        self.label_dataset(dataset, labels)
-
         clusters = {}
         for label in labels:
             clusters[label] = [ds for ds in dataset if ds['cluster_label'] == label]
         
         return clusters
 
-    @deprecated(reason="Use generic version instead")
-    def cluster_locations(self, locations:List[Dict]) -> ClusterGroup:
-        '''Returns a dictionary with identified clusters and their locations copied from the input'''
-        if locations is None or len(locations) == 0:
-            # raise Exception("locations has to contain something")
-            return {}
-
-        features = self.extract_location_features(locations)
-        
-        labels = self.create_labels(features)
-        self.label_dataset(locations, labels)
-
-        return self.group_by_clusters(locations, labels)
-       
-    @deprecated(reason="Use generic version instead") 
-    def cluster_times(self, times:List[Dict]) -> ClusterGroup:
-        '''Returns a dictionary with identified clusters and their times copied from the input'''
-        features = self.extract_time_features(times)
-    
-        labels = self.create_labels(features)
-        self.label_dataset(times, labels)
-
-        return self.group_by_clusters(times, labels)
-
     def cluster_dataset(self, dataset:List[Dict], features:List[str]) -> ClusterGroup:
         '''
         Returns the identified clusters containing a subset of nodes from the dataset.
@@ -140,6 +71,8 @@ class Clusterer:
         arr = self._extract_features(dataset, features)
 
         labels = self.create_labels(arr)
+        
+        self.label_dataset(dataset, labels)
 
         return self.group_by_clusters(dataset, labels)
 

src/data-hub/community-detection-microservice/app/routes/clustersets.py → src/data-hub/role-stage-discovery-microservice/app/routes/clustersets.py

@@ -4,23 +4,9 @@ from db.entities import ClusterSet
 
 repo = Repository()
 
-def get():
-    return [c.to_serializable_dict() for c in repo.get_clustersets()]
-
-def get_names():
-    return repo.get_clusterset_names()
-
-
-def get_by_name2(name):
+def get_by_name(name):
     res = repo.get_clusters_for_layer(name)
     if res is None or len(res) == 0:
         return Response(status=404)
     else:
         return [c.to_serializable_dict() for c in res]
-
-def get_by_name(name):
-    res = repo.get_clusterset(name)
-    if res is not None:
-        return res.to_serializable_dict() 
-    else:
-        return Response(status=404)
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/routes/functions.py

+
+def run_agi_clustering_and_graph_creation():
+    pass

src/data-hub/community-detection-microservice/app/routes/layers.py → src/data-hub/role-stage-discovery-microservice/app/routes/layers.py

@@ -15,7 +15,6 @@ def post():
 def _insert_layer(layer_data: dict):
     '''Converts object keys from external source and inserts into database.'''
     layer_data['layer_name'] = layer_data.pop('LayerName')
-    # layer_data['nodes'] = layer_data.pop('Nodes')
     layer_data['properties'] = layer_data.pop('Properties')
 
     repo.add_layer(Layer(layer_data))

src/data-hub/community-detection-microservice/app/routes/timeslices.py → src/data-hub/role-stage-discovery-microservice/app/routes/timeslices.py

@@ -4,23 +4,8 @@ from db.entities import TimeSlice
 
 repo = Repository()
 
-
-def get():
-    return [e.to_serializable_dict() for e in repo.get_time_slices()]
-
-
-def get_by_name(layername):
-    res = repo.get_time_slices_by_name(layername)
-    # print(len(res))
-    
-    if res is not None and len(res) != 0:
-        return [e.to_serializable_dict() for e in res]
-    else:
-        return Response(status=404)
-
-def get_by_name2(name):
+def get_by_name(name):
     res = repo.get_time_slices_by_name(name)
-    # print(len(res))
     
     if res is not None and len(res) != 0:
         return [e.to_serializable_dict() for e in res]

src/data-hub/community-detection-microservice/app/run_clustering.py → src/data-hub/role-stage-discovery-microservice/app/run_clustering.py

@@ -5,16 +5,13 @@ if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
 
 import json
-from db.entities import *
+from db.entities import Layer, Cluster
 from typing import List, Dict, Tuple
-from db.repository import Repository, AgiRepository
+from db.repository import Repository
 from processing.clustering.clusterer import Clusterer
 
 
-DEBUG = False
-
 repo = Repository()
-test_repo = AgiRepository()
 
 
 def run_generic_clustering():
@@ -29,7 +26,6 @@ def run_generic_clustering():
             continue
 
         clusters = run_clustering_for_layer(layer)
-        # cluster_set = ClusterSet(layer.layer_name, clusters)
         store_generic_clusters(clusters)
 
 
@@ -44,74 +40,10 @@ def run_clustering_for_layer(layer: Layer) -> List[Cluster]:
 
     return [Cluster(layer.layer_name, key, value) for key, value in res.items()]
 
+
 def store_generic_clusters(clusters: List[Cluster]):
     repo.add_clusters(clusters)
-    # with open(f'clusterset_{cluster_set.layer_name}.txt', 'w') as file:
-    #     file.write(json.dumps(cluster_set.to_serializable_dict()))
-
-
-def run_location_clustering():
-    user_clusterer = Clusterer()
-
-    all_location_traces = repo.get_locations()
-
-    cluster_result = user_clusterer.cluster_locations(
-        [l.to_serializable_dict() for l in all_location_traces])
-
-    clusters = [LocationCluster(key, value)
-                for key, value in cluster_result.items()]
-
-    store_clusters('locations', clusters)
-
-
-def run_time_clustering():
-    clusters: List[TimeCluster] = []
-    user_clusterer = Clusterer(epsilon=600)  # clustered within 10 minutes
-
-    all_location_traces = repo.get_locations()
-
-    # for each date in timestamp list
-    dates = {trace.timestamp.date() for trace in all_location_traces}
-    for cur_date in dates:
-        traces_for_cur_date = [
-            trace for trace in all_location_traces if trace.timestamp.date() == cur_date]
-
-        # for each hour of that day
-        for cur_hour in list(range(24)):
-            traces_for_time_slice = [
-                trace for trace in traces_for_cur_date if trace.timestamp.hour == cur_hour]
-
-            if len(traces_for_time_slice) == 0:
-                continue
-
-            # clustering per hour
-            cluster_result = user_clusterer.cluster_times(
-                [t.to_serializable_dict() for t in traces_for_time_slice])
-            cur_clusters = [TimeCluster(cur_date, cur_hour, key, value)
-                            for key, value in cluster_result.items()]
-
-            clusters.extend(cur_clusters)
-
-    store_clusters('times', clusters)
-
-
-def store_clusters(type: str, clusters: List):
-    if DEBUG:
-        print(clusters)
-        return
-
-    if type == 'locations':
-        for c in clusters:
-            repo.add_location_cluster(c)
-
-    if type == 'times':
-        for c in clusters:
-            repo.add_time_cluster(c)
 
 
 if __name__ == "__main__":
     run_generic_clustering()
-
-    # TODO cleanup
-    # run_location_clustering()
-    # run_time_clustering()

src/data-hub/role-stage-discovery-microservice/app/tests/test_cluster.py

+import unittest
+import sys
+sys.path.insert(1, '../')
+
+# python -m unittest discover
+from db.entities import Cluster
+
+from datetime import date, datetime
+import json
+
+
+class TestCluster(unittest.TestCase):
+    def test_init_Cluster(self):
+        c = Cluster('layer1', 1, [1, 2, 3])
+
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+
+if __name__ == '__main__':
+    unittest.main()

src/data-hub/community-detection-microservice/app/tests/test_clusterer.py → src/data-hub/role-stage-discovery-microservice/app/tests/test_clusterer.py

@@ -5,12 +5,39 @@ for path in ['../', './']:
 
 # python -m unittest discover
 from processing.clustering.clusterer import Clusterer
+import numpy as np
 
 class TestClusterer(unittest.TestCase):
     clusterer:Clusterer = None
 
     def setUp(self):
-        self.clusterer = Clusterer(epsilon=10, min_points=2)
+        self.clusterer = Clusterer(min_points=2)
+
+    #region _extract_features
+
+    def test_extract_features_emptyDataset_noResults(self):
+        features = self.clusterer._extract_features(dataset=[], features=['test'])
+        np.testing.assert_equal(np.asarray([]), features)
+
+    def test_extract_features_emptyFeatures_singleEmptyResult(self):
+        features = self.clusterer._extract_features(dataset=[{'a':1, 'b':2}], features=[])
+        np.testing.assert_equal(np.asarray([[]]), features)
+
+    def test_extract_features_singleFeature_Projection(self):
+        features = self.clusterer._extract_features(dataset=[{'a':1, 'b':2}], features=['a'])
+        np.testing.assert_equal(np.asarray([[1]]), features)
+
+    def test_extract_features_singleFeature_Projection_2(self):
+        features = self.clusterer._extract_features(dataset=[{'a':1, 'b':2}, {'a':3, 'b':4}], features=['a'])
+        np.testing.assert_equal(np.asarray([[1], [3]]), features)
+        
+    def test_extract_features_multFeature_Projection(self):
+        features = self.clusterer._extract_features(dataset=[{'a':0, 'b':2, 'c':4}, {'a':1, 'b':3, 'c':5}], features=['a','c'])
+        np.testing.assert_equal(np.asarray([[0,4], [1,5]]), features)
+    
+    #endregion _extract_features
+
+    #region create_labels
 
     def test_create_labels_noneInput_noneOutput(self):
         labels = self.clusterer.create_labels(None)
@@ -19,51 +46,65 @@ class TestClusterer(unittest.TestCase):
     def test_create_labels_emptyInput_emptyOutput(self):
         labels = self.clusterer.create_labels([])
         self.assertEqual([], labels)
-
-    def test_create_labels_singleInput_singleCluster(self):
-        features = self.clusterer.extract_location_features([self.location(1,2)])
-        labels = self.clusterer.create_labels(features)
-        self.assertEqual(1, len(labels))
+    
+    def test_create_labels_singleInput_error(self):
+        clusterer = Clusterer(min_points=2) 
+        features =  clusterer._extract_features(dataset=[self.location(1,2)], features=self.get_location_features())
+        with self.assertRaises(ValueError):
+            # Fails because (min_pts > |input elements|)
+            clusterer.create_labels(features)
+            
+    def test_create_labels_singleInput_error_2(self):
+        clusterer = Clusterer(min_points=1)
+        features =  clusterer._extract_features(dataset=[self.location(1,2)], features=self.get_location_features())
+        with self.assertRaises(ValueError):
+            # Fails because fitting does not work internally 
+            clusterer.create_labels(features)
 
     def test_create_labels_nearInputs_singleCluster(self):
         locations = [self.location(1,2), self.location(2,2)]
 
-        features = self.clusterer.extract_location_features(locations)
+        features = self.clusterer._extract_features(dataset=locations, features=self.get_location_features())
         labels = self.clusterer.create_labels(features)
 
         self.assertEqual(2, len(labels))
         self.assertEqual(labels[0], labels[1])
 
     def test_create_labels_nearInputs_twoClusters(self):
-        locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
+        locations = [self.location(1,2), self.location(2,2), self.location(20,20), self.location(20,23)]
 
-        features = self.clusterer.extract_location_features(locations)
+        features = self.clusterer._extract_features(dataset=locations, features=self.get_location_features())
         labels = self.clusterer.create_labels(features)
 
-        self.assertEqual(3, len(labels))
+        self.assertEqual(4, len(labels))
         self.assertEqual(labels[0], labels[1])
+        self.assertEqual(labels[2], labels[3])
         self.assertNotEqual(labels[0], labels[2])
 
-    def test_label_locations_NoneLocations_NoException(self):
+    #endregion create_labels
+
+    #region label_dataset
+
+    def test_label_dataset_NoneLocations_NoException(self):
         self.clusterer.label_dataset(None, [])
 
-    def test_label_locations_NoneLabels_NoException(self):
+    def test_label_dataset_NoneLabels_NoException(self):
         self.clusterer.label_dataset([], None)
 
-    def test_label_locations_emptyInput_emptyOutput(self):
+    def test_label_dataset_emptyInput_emptyOutput(self):
         locations = []
         self.clusterer.label_dataset(locations, [])
         self.assertEqual(0, len(locations))
 
-    def test_label_locations_diffInputLengths_ValueError_1(self):
+    def test_label_dataset_diffInputLengths_ValueError_1(self):
         with self.assertRaises(ValueError):
             self.clusterer.label_dataset([], [1])
 
-    def test_label_locations_diffInputLengths_ValueError_2(self):
+    def test_label_dataset_diffInputLengths_ValueError_2(self):
         with self.assertRaises(ValueError):
             self.clusterer.label_dataset([self.location(1,2)], [])
 
-    def test_label_locations_multInput_correctlyLabeled(self):
+    def test_label_dataset_multInput_correctlyLabeled(self):
         locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
         labels = [17,2,20]
 
@@ -72,53 +113,76 @@ class TestClusterer(unittest.TestCase):
         self.assertEqual(3, len(locations))
         self.assertHaveLabelsAsNewKey(locations, labels)
 
-    def test_cluster_locations_multInput_correctlyLabeled(self):
-        locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
-        labels = [0,0,-1]
-
-        res = self.clusterer.cluster_locations(locations)
-        
-        self.assertHaveLabelsAsNewKey(locations, labels)
-        self.assertDictEqual(res, {0: [{'latitude': 1, 'longitude': 2, 'cluster_label': 0}, {'latitude': 2, 'longitude': 2, 'cluster_label': 0}], -1: [{'latitude': 20, 'longitude': 20, 'cluster_label': -1}]})
-
-    def test_cluster_times_multInput_correctlyLabeled(self):
-        times = [self.time(123), self.time(128), self.time(223)]
-        labels = [0,0,-1]
+    #endregion label_dataset
 
-        res = self.clusterer.cluster_times(times)
-        
-        self.assertHaveLabelsAsNewKey(times, labels)
-        self.assertDictEqual(res, {0: [{'timestamp': 123, 'cluster_label': 0}, {'timestamp': 128, 'cluster_label': 0}], -1: [{'timestamp': 223, 'cluster_label': -1}]})
+    #region cluster_dataset
 
     def test_cluster_dataset_locationsMultInput_correctlyLabeled(self):
-        locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
-        labels = [0,0,-1]
+        locations = [self.location(1,2), self.location(2,2), self.location(20,20), self.location(20,21)]
+        labels = [0,0,1,1]
+        exp_res = {0:locations[0:2], 1:locations[2:4]}
+
+        res = self.clusterer.cluster_dataset(locations, self.get_location_features())
 
-        res = self.clusterer.cluster_dataset(locations, ['latitude', 'longitude'])
-        
         self.assertHaveLabelsAsNewKey(locations, labels)
-        self.assertDictEqual(res, {0: [{'latitude': 1, 'longitude': 2, 'cluster_label': 0}, {'latitude': 2, 'longitude': 2, 'cluster_label': 0}], -1: [{'latitude': 20, 'longitude': 20, 'cluster_label': -1}]})
+        self.assertClusteringResult(exp_res, res)
 
     def test_cluster_dataset_timesMultInput_correctlyLabeled(self):
-        times = [self.time(123), self.time(128), self.time(223)]
-        labels = [0,0,-1]
+        times = [self.time(123), self.time(128), self.time(223), self.time(225)]
+        labels = [0,0,1,1]
+        exp_res = {0:times[0:2], 1:times[2:4]}
 
-        res = self.clusterer.cluster_dataset(times, ['timestamp'])
+        res = self.clusterer.cluster_dataset(times, self.get_time_features())
         
         self.assertHaveLabelsAsNewKey(times, labels)
-        self.assertDictEqual(res, {0: [{'timestamp': 123, 'cluster_label': 0}, {'timestamp': 128, 'cluster_label': 0}], -1: [{'timestamp': 223, 'cluster_label': -1}]})
+        self.assertClusteringResult(exp_res, res)
 
+    def test_cluster_dataset_locationsMultInput_correctlyLabeled_2(self):
+        return
+        # TODO why is the single location added to the last cluster?
+        clusterer = Clusterer(3)
+        locations = [self.location(1,2), self.location(2,2), self.location(2,2), self.location(20,20), self.location(20,21),  self.location(20,20), self.location(400,1000), self.location(200,1), self.location(200,2), self.location(201,-1)]
+        labels = [0,0,1,1]
+        exp_res = {0:locations[0:2], 1:locations[2:4]}
+
+        res = clusterer.cluster_dataset(locations, self.get_location_features())
+        print(res)
+
+        self.assertHaveLabelsAsNewKey(locations, labels)
+        self.assertClusteringResult(exp_res, res)
+
+    #endregion cluster_dataset
+
+    #region helper methods
 
-    # helper methods:
     def location(self, lat, long_) -> dict:
         return {'latitude': lat, 'longitude':long_}
 
+    def get_location_features(self):
+        return ['latitude', 'longitude']
+
     def time(self, ts) -> dict:
         return {'timestamp': ts}
 
+    def get_time_features(self):
+        return ['timestamp']
+
     def assertHaveLabelsAsNewKey(self, locations, labels):
+        self.assertEqual(len(labels), len(locations))
+
         for i in range(len(locations)):
             self.assertEqual(labels[i], locations[i]['cluster_label'])
 
+    def assertClusteringResult(self, expected, actual):
+        self.assertEqual(len(expected), len(actual))
+
+        for k in expected.keys():
+            if k not in actual:
+                self.fail(f"Cluster key ({k}, {type(k)}) not in result.")
+            
+            self.assertListEqual(expected[k], actual[k])
+   
+    #endregion helper methods
+
 if __name__ == '__main__':
     unittest.main()

src/data-hub/community-detection-microservice/deployment/deployment.yml → src/data-hub/role-stage-discovery-microservice/deployment/deployment.yml

 apiVersion: v1
 kind: Service
 metadata:
-  name: community-detection
+  name: role-stage-discovery
 spec:
   type: LoadBalancer
   selector:
-      app: community-detection
+      app: role-stage-discovery
   ports:
     - name: http
       port: 80
       targetPort: 5000
-      nodePort: 30109
+      nodePort: 30103
       protocol: TCP
 ---
 apiVersion: apps/v1
 kind: Deployment
 metadata:
-  name: community-detection
+  name: role-stage-discovery
 spec:
   replicas: 1
   selector:
     matchLabels:
-      app: community-detection
+      app: role-stage-discovery
   template:
     metadata:
       labels:
-        app: community-detection
+        app: role-stage-discovery
     spec:
       containers:
-      - name: community-detection
-        image: alexx882/community-detection-microservice
+      - name: role-discovery
+        image: alexx882/role-stage-discovery-microservice
         ports:
         - containerPort: 5000
 ---
 apiVersion: v1
 kind: Service
 metadata:
-  name: community-detection-db
+  name: role-stage-discovery-db
 spec:
   type: LoadBalancer
   selector:
-      app: community-detection-db
+      app: role-stage-discovery-db
   ports:
     - name: http
       port: 27017
       targetPort: 27017
-      nodePort: 30110
+      nodePort: 30104
       protocol: TCP
 ---
 apiVersion: apps/v1
 kind: Deployment
 metadata:
-  name: community-detection-db
+  name: role-stage-discovery-db
 spec:
   replicas: 1
   selector:
     matchLabels:
-      app: community-detection-db
+      app: role-stage-discovery-db
   template:
     metadata:
       labels:
-        app: community-detection-db
+        app: role-stage-discovery-db
     spec:
       containers:
-      - name: community-detection-db
+      - name: role-stage-discovery-db
         image: mongo
         env:
         - name: MONGO_INITDB_ROOT_USERNAME

src/data-hub/semantic-linking-microservice/app/db/repository.py

+import pymongo
+import network_constants as netconst
+from database.MongoRepositoryBase import MongoRepositoryBase
+import json
+
+from typing import List
+
+class Repository(MongoRepositoryBase):
+    '''This is a repository for MongoDb.'''
+
+    def __init__(self):
+        super().__init__(netconst.SEMANTIC_LINKING_DB_HOSTNAME,
+                         netconst.SEMANTIC_LINKING_DB_PORT,
+                         'semanticLinkingDb')
+
+        self._layer_collection = 'layer'
+

src/data-hub/semantic-linking-microservice/app/requirements.txt

-flask
-connexion[swagger-ui]
-pika
+astroid==2.4.2
+attrs==19.3.0
+certifi==2020.4.5.2
+chardet==3.0.4
+click==7.1.2
+clickclick==1.2.2
+colorama==0.4.3
+connexion==2.7.0
+Flask==1.1.2
+idna==2.9
+importlib-metadata==1.6.1
+inflection==0.5.0
+isort==4.3.21
+itsdangerous==1.1.0
+Jinja2==2.11.2
+jsonschema==3.2.0
+lazy-object-proxy==1.4.3
+MarkupSafe==1.1.1
+mccabe==0.6.1
+openapi-spec-validator==0.2.8
+pika==1.1.0
+pylint==2.5.3
+pymongo==3.10.1
+pyrsistent==0.16.0
+PyYAML==5.3.1
+requests==2.23.0
+six==1.15.0
+swagger-ui-bundle==0.0.6
+toml==0.10.1
+typed-ast==1.4.1
+urllib3==1.25.9
+Werkzeug==1.0.1
+wrapt==1.12.1
+zipp==3.1.0

src/data-hub/semantic-linking-microservice/app/rest/debug.py

 from flask import request
+from db.repository import Repository
+
+repo = Repository()
 
 def echo():
     return request.json

src/data-hub/semantic-linking-microservice/deployment/deployment.yml

@@ -31,4 +31,44 @@ spec:
       - name: semantic-linking
         image: alexx882/semantic-linking-microservice
         ports:
-        - containerPort: 5000
+        - containerPort: 5000
\ No newline at end of file
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: semantic-linking-db
+spec:
+  type: LoadBalancer
+  selector:
+      app: semantic-linking-db
+  ports:
+    - name: http
+      port: 27017
+      targetPort: 27017
+      nodePort: 30102
+      protocol: TCP
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: semantic-linking-db
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: semantic-linking-db
+  template:
+    metadata:
+      labels:
+        app: semantic-linking-db
+    spec:
+      containers:
+      - name: semantic-linking-db
+        image: mongo
+        env:
+        - name: MONGO_INITDB_ROOT_USERNAME
+          value: root
+        - name: MONGO_INITDB_ROOT_PASSWORD
+          value: root
+        ports:
+        - containerPort: 27017
\ No newline at end of file

src/data-hub/stage-discovery-microservice/Dockerfile

-FROM python:3
-LABEL maintainer="Alexander Lercher"
-
-ENV http_proxy http://proxy.uni-klu.ac.at:3128/
-ENV https_proxy http://proxy.uni-klu.ac.at:3128/
-
-RUN apt-get update
-RUN pip install flask
-RUN pip install connexion[swagger-ui]
-
-EXPOSE 5000
-
-WORKDIR /app
-COPY src/data-hub/stage-discovery-microservice/app/ /app/
-RUN chmod a+x main.py
-
-CMD ["python", "./main.py"]
\ No newline at end of file

src/data-hub/stage-discovery-microservice/README.md

-# Stage Discovery Microservice
-The stage discovery microservice detects individual activities between roles inside of a concept, e.g. teacher giving an assignment to students.
-
-## Technologies
-- Python 3.x
-- Python module Flask
-- Python module Connexion with Swagger
-- Docker
-- Kubernetes
\ No newline at end of file

src/data-hub/stage-discovery-microservice/app/configs/swagger.yml

-swagger: "2.0"
-info:
-  title: Stage Discovery microservice
-  description: This is the documentation for the stage discovery microservice.
-  version: "1.0.0"
-
-consumes:
-  - "application/json"
-produces:
-  - "application/json"
-
-basePath: "/api"
-
-paths:
-  /debug:
-    post:
-      operationId: "debug.echo"
-      tags:
-        - "Echo"
-      summary: "Echo function for debugging purposes"
-      description: "Echoes the input back to the caller."
-      parameters:
-        - in: body
-          name: "Object"
-          required: true
-          schema:
-            type: object
-      responses:
-        200:
-          description: "Successful echo of request data"

src/data-hub/stage-discovery-microservice/app/debug.py

-from flask import request
-
-def echo():
-    return request.json
-
- 
\ No newline at end of file

src/data-hub/stage-discovery-microservice/app/main.py

-import connexion
-
-# load swagger config
-app = connexion.App(__name__, specification_dir='configs/')
-app.add_api('swagger.yml')
-
-@app.route('/', methods=['GET'])
-def api_root():
-    return 'Endpoint of stage-discovery-microservice!'
-
-# start app
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=True)

src/data-hub/stage-discovery-microservice/deployment/deployment.yml

-apiVersion: v1
-kind: Service
-metadata:
-  name: stage-discovery
-spec:
-  type: LoadBalancer
-  selector:
-      app: stage-discovery
-  ports:
-    - name: http
-      port: 80
-      targetPort: 5000
-      nodePort: 30103
-      protocol: TCP
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: stage-discovery
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: stage-discovery
-  template:
-    metadata:
-      labels:
-        app: stage-discovery
-    spec:
-      containers:
-      - name: stage-discovery
-        image: alexx882/stage-discovery-microservice
-        ports:
-        - containerPort: 5000
\ No newline at end of file

src/modules/network_constants.py

@@ -7,15 +7,17 @@ RABBIT_MQ_PORT = 5672
 ## Trace Retrieval
 TRACE_RETRIEVAL_HOSTNAME = 'trace-retrieval'
 TRACE_RETRIEVAL_REST_PORT = 80
-TRACE_RETRIEVAL_DB_HOSTNAME = 'trace-retrieval-db'
+TRACE_RETRIEVAL_DB_HOSTNAME = f'{TRACE_RETRIEVAL_HOSTNAME}-db'
 TRACE_RETRIEVAL_DB_PORT = 27017
-# TRACE_RETRIEVAL_DB_HOSTNAME = 'articonf1.itec.aau.at'
-# TRACE_RETRIEVAL_DB_PORT = 30003
 
-## Community Detection
-COMMUNITY_DETECTION_HOSTNAME = 'community-detection'
-COMMUNITY_DETECTION_REST_PORT = 80
-COMMUNITY_DETECTION_DB_HOSTNAME = 'community-detection-db'
-COMMUNITY_DETECTION_DB_PORT = 27017
-# COMMUNITY_DETECTION_DB_HOSTNAME = 'localhost'
-# COMMUNITY_DETECTION_DB_PORT = 30110
+## Semantic Linking
+SEMANTIC_LINKING_HOSTNAME = 'semantic-linking'
+SEMANTIC_LINKING_REST_PORT = 80
+SEMANTIC_LINKING_DB_HOSTNAME = f'{SEMANTIC_LINKING_HOSTNAME}-db'
+SEMANTIC_LINKING_DB_PORT = 27017
+
+## Role Stage Discovery
\ No newline at end of file
+ROLESTAGE_DISCOVERY_HOSTNAME = 'role-stage-discovery'
+ROLESTAGE_DISCOVERY_REST_PORT = 80
+ROLESTAGE_DISCOVERY_DB_HOSTNAME = f'{ROLESTAGE_DISCOVERY_HOSTNAME}-db'
+ROLESTAGE_DISCOVERY_DB_PORT = 27017
\ No newline at end of file